CDMP
Certified Data Management Professional
Data Management
Examination Guide
Data Exam Series Vol. 1
The Education & Research
Affiliate of DAMA International
The Data Management Examination Guide
Acknowledgement is made for permission to use the full exam outline from the jointly
developed DAMA International – Institute for Certification of Computing Professionals
(ICCP) Data Management exam outline, copyright © 2006 ICCP.
Written by Diane C. Johnson, PMP, for DAMA International & DAMA International
Foundation.
Published by DAMA International & DAMA International Foundation, Bellevue, WA,
U.S.A.
Data Management Examination Guide
Data Exam Series, Vol. 1
To order copies, please contact
DAMAi@dama.org
PO Box 5786
Bellevue, WA 98006-5786
1-425-562-2636
www.dama.org
For exam administration questions, please contact
office@iccp.org
or call 1.800.843.8227
2350 E. Devon Avenue,
Suite 115, Des Plaines,
IL 60018 USA
www.iccp.org
Copyright © 2006 by DAMA International & DAMA International Foundation
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form, or by any means, electronic, mechanical,
photocopying, recording, or otherwise, without the prior consent of the publisher.
ISBN 0-9676674-3-7
First Edition, April 2006
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The Data Management Examination Guide
Table of Contents
Introduction......................................................................................................................... 4
How Do You Obtain a CDMP? ...................................................................................... 5
CDMP Examination Criteria........................................................................................... 5
Additional CDMP Certification Criteria......................................................................... 6
Recommended Exams Based on Candidate’s Work Experience.................................... 8
Preparation for Taking Exams ........................................................................................ 9
Taking CDMP Exams ..................................................................................................... 9
Professional Development / Recertification ................................................................. 10
CDMP Contact Information.......................................................................................... 10
ICCP Data Management Specialty Examination Outline................................................. 12
How To Read The Data Management Subject Outline ................................................ 12
Data Management Exam Subject Outline..................................................................... 13
1.0 Data Management Function........................................................................................ 21
Overview....................................................................................................................... 21
Topics............................................................................................................................ 21
Questions....................................................................................................................... 22
Quick Answers.............................................................................................................. 33
Detailed Answers .......................................................................................................... 34
2.0 Data & Metadata Infrastructures Creation / Maintenance .......................................... 42
Overview....................................................................................................................... 42
Topics............................................................................................................................ 42
Questions....................................................................................................................... 43
Quick Answers.............................................................................................................. 51
Detailed Answers .......................................................................................................... 52
3.0 Data Analysis and Modeling....................................................................................... 57
Overview....................................................................................................................... 57
Topics............................................................................................................................ 57
Questions....................................................................................................................... 58
Quick Answers.............................................................................................................. 76
Detailed Answers .......................................................................................................... 77
4.0 Data / Metadata Infrastructure Management .............................................................. 88
Overview....................................................................................................................... 88
Topics............................................................................................................................ 88
Questions....................................................................................................................... 89
Quick Answers.............................................................................................................. 96
Detailed Answers .......................................................................................................... 97
5.0 Information Quality Management............................................................................. 102
Overview..................................................................................................................... 102
Topics.......................................................................................................................... 102
Questions..................................................................................................................... 103
Quick Answers............................................................................................................ 114
Detailed Answers ........................................................................................................ 115
Selected Bibliography..................................................................................................... 122
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The Data Management Examination Guide
Introduction
The Certified Data Management Professional (CDMP) credential validates knowledge
and experience of Data Management Professionals. CDMP Credentials can be a doorway
to opportunities either measuring your standing by demonstrating Mastery level or
providing a starting point for new professions through a Practitioner designation.
The Certified Data Management Professional (CDMP) credential is awarded to those
who qualify based on a combination of criteria including education, experience and testbased examination of professional level knowledge. To maintain certified status and
continued use of the credential, an annual recertification fee along with a 3-year cycle of
continuing education and professional activity is required. The Data Management
Association International (DAMA) authorizes the Certified Data Management
Professional certification program and granting of the CDMP designation in partnership
with the Institute for Certification of Computing Professionals (ICCP), which administers
testing and recertification.
The ICCP Data Management exam is meant to be an experience exam, meaning that it
tests what you know at the time. This study guide is meant to be a refresher to test taking
and the concepts behind data management. You can focus on the sections that you need
to learn, or take the practise exam to see where your strengths lie. The study guide is
broken down into the five major sections of the exam:
1.0 Data Management Function
2.0 Data & Metadata Infrastructures Creation / Maintenance
3.0 Data Analysis and Modeling
4.0.Data / Metadata Infrastructure Management
5.0 Information Quality Management
The DAMA International Foundation welcomes feedback on this Study Guide, as
revisions will occur in the future. We encourage you to let us know how you are using
these materials and how they might be improved. Your comments can be sent to:
Vice President of Education, VP_Education_Services@DAMA.org
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The Data Management Examination Guide
Overview of the Certified Data Management Professional
(CDMP) Certification Program
How Do You Obtain a CDMP?
The CDMP certification process is as follows:
1.
2.
3.
4.
5.
Fill out an application (from www.dama.org or www.iccp.org).
Arrange to take the exam(s).
Pass the IT Core exam (required).
Take two specialty exams.
One specialty exam must be taken from
a. Data Management
b. Data Warehousing
c. Database Administration
6. Meet the experience and education qualifications.
7. Sign the ICCP code of ethics.
There is a professional development / recertification aspect to keeping your certification
current after you are certified. This recertification activity is handled through the ICCP
office.
CDMP Examination Criteria
Three ICCP exams must be passed with the following scores:
Score
Pass all exams at 50% or higher
Pass all exams at 70% or higher
Credential Earned
CDMP Practitioner Certificate
CDMP Mastery Certificate
The CDMP Practitioner certification is awarded to professionals who scored above 50%
on all three exams. These individuals can contribute as a team member on assigned tasks
for they have a working knowledge of concepts, skills and techniques in a particular data
specialization.
The CDMP Mastery certification is awarded to professionals who scored 70% or higher
on all three exams. These individuals have the ability to lead and mentor a team of
professionals as they have mastered the concepts, skills and practices of their data
specialization.
Exams may be retaken to improve your score and go from the Practitioner to the Mastery
certificate level. You may be able to substitute select vendor certifications for up to one
specialty exam.
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The Data Management Examination Guide
Additional CDMP Certification Criteria
The following criteria must also be met in order to qualify for the CDMP:
CDMP Criteria
# Years Data
Professional Work
Experience
Substitute Up to 2 Years
–Bachelor or Master
Degree in an
appropriate discipline
for Work Experience
Recertification
Required
Continuing Professional
Education/Activity
Required
ICCP Code of Ethics
CDMP Practitioner
Certificate
2
CDMP Mastery
Certificate
4+
2
2
Yes
Yes
120 hours every 3-year
cycle
120 hours every 3-year
cycle
Yes
Yes
Sample Qualifications for the CDMP
Other qualifications may be accepted. Check with the DAMA contacts or ICCP office.
Education
Bachelor of Science Degree
Major in:
Computer Science
Information Systems
Management Information Systems
Information and Communications Technology
Major in another discipline with minor in any of the above
Masters Degree
Computer Science
Information Systems
Information Resource Management
Information and Communications Technology
MBA with concentration in one of the above
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The Data Management Examination Guide
Work Experience
Sample qualifying position/role titles:
VP, Director, or Manager of Data Management
Data Architect, Data Administrator, Data Analyst, Data Modeler
Data Specialist, Database Administrator, Data Warehousing Analyst
Systems Architect, Systems Analyst, Project Manager, Project Leader
Business Analyst, Repository Analyst, Repository Architect
Professional Examinations
The CDMP requires three ICCP exams: IT Core, one specified data oriented exam, and
one other exam. If you already passed one or more ICCP exams, these exams can be
used toward a CDMP if considered current by ICCP standards, and the exams are listed
within your CDMP area of specialization. For information on your status, contact the
ICCP.
If you wish to demonstrate expertise in exam specialty areas specifically, the ICCP will
issue Expert (Proficiency) Certificates for each specialty exam passed at 70% or higher.
If you wish to know how these exams were developed, go to
(http://www.iccp.org/iccpnew/iwg2.html). These exams are product and vendor neutral,
and international in scope.
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The Data Management Examination Guide
Suggested Exams Based on Candidate’s Work Experience
The following table shows the Data Management areas by which ICCP exams are either
suggested (S) or your choice (C) for a total of three exams. The IT Core exam is required
for all candidates. Your work experience in the field will let you determine what exams
you are best suited to pass.
ICCP Exams
Mgmt
Architecture
Data
Analysis
& Design
DBA
Data
Warehousing
Metadata /
Repository
Mgmt
Data /
Information
Quality
(Future)
Req’d
Req’d
Req’d
Req’d
Req’d
Req’d
Req’d
S
S
S
C
C
S
S
Database
Administration
C
C
S
C
C
C
Data Warehousing
C
C
C
S
C
C
C
IT Core
Specialty Exams
Data Management
Integrated Project
Mgmt
C
C
C
IT Management
C
C
C
Systems
Development
C
Object Oriented
Analysis & Design
C
C
C
C
Systems Security
C
Future ICCP
Exams
Business
Intelligence &
Analytics
S
Data & Information
Quality
Acceptable Exam
Substitutes (Third
Party)
C
C
C
C
C
C
S
C
C
C
C
C (future: e.g.
MIT or Berkeley
– DQ programs)
Page 8 of 122
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The Data Management Examination Guide
Accepted Vendor/Training Certifications
Any of the following certifications may be substituted for one of the "candidate's choice"
specialty exams required for the CDMP. Other certification programs may be accepted,
but need to be evaluated. Check with the ICCP office or the DAMA contacts.
IBM
- IBM Certified Database Administrator - DB2 Universal Database
- IBM Certified Advanced Database Administrator – DB2 Universal Database
- IBM Certified Solutions Expert - DB2 Universal Database
- IBM Certified Solutions Expert - DB2 Content Manager
Information Engineering Services Pty Ltd
- Certified Business Data Modeller
Insurance Data Management Association (IDMA)
- Certified Insurance Data Manager
Microsoft
- Microsoft Certified Database Administrator
NCR (Teradata)
- Teradata Certified Professional
Oracle
- Oracle (xx) Certified Professional
- Oracle9i Database Administrator Certified Professional (for Practitioner Level CDMP)
- Oracle9i Database Administrator Certified Master (for Mastery Level CDMP)
Project Management Institute
- Project Management Professional (PMP)
Preparation for Taking Exams
There are various ways of learning the process of taking ICCP exams:
• Sponsor ICCP Exam Review courses for your DAMA chapter membership
• Refer to the exam subject outlines (at level 1 & 2) posted on
http://www.iccp.org/iccpnew/outlines.html to become familiar with the subject
coverage of each exam
• Contact the ICCP for the CDMP Study Guide which covers all the exams in the
CDMP program and has sample exams/questions for self-study
• Contact DAMA International for the Data Management Exam Study Guide. Other
individual data exam study guides are planned for the future.
The ICCP exams are also offered at the DAMA International Symposiums.
Taking CDMP Exams
ICCP Testing can be done anywhere in the world, with an approved ICCP Proctor to
verify physical identity and supervise/invigilate the delivery of the examination.
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The Data Management Examination Guide
A DAMA chapter can set up exam sessions during their chapter meetings. What is
needed is a volunteer proctor from the chapter. A proctor is an individual authorized by
ICCP to oversee the writing of an exam by an ICCP exam taker. This person must meet
specific guidelines (http://www.iccp.org/iccpnew/testing.html) and be willing to
supervise the exam taker. The ICCP reserves the right to reject proposed proctors.
Contact office@iccp.org or phone 847.299.4227 or 800.843.8227 if you require
assistance in determining an appropriate proctor.
The exams run off the USB drive of an individual’s laptop. There are 110 questions with
110 being scored and 10 are beta questions to complete in 90 minutes. You will not know
which type of question you are answering. Questions and possible distracters (answers)
are randomly listed in a different order for each exam taker. Therefore, although this
guide contains sample questions that allow for “all or none of the above” type answers
meant for study purposes, you will not find this type of answer to choose from on the
actual exam.
Computer based testing allows for the immediate scoring after the exam is taken. An
ICCP Performance Profile is then available for downloading, and one will be sent later to
the individual by the ICCP. This Profile shows your exam strengths and weaknesses.
Professional Development / Recertification
To keep your CDMP current, you must earn 120 approved contact hours of continuing
education over a 3-year period. Many educational activities count including DAMA
Symposiums and chapter meetings. For further information, contact the ICCP
(office@iccp.org) for an ICCP Recertification Guidelines Booklet or go to
www.iccp.org/iccpnew/Recertification%20Guidelines2005.pdf.
Recertification credits can be entered on an ICCP Educational Activity Form or through
www.iccp.org/cgi-win/pdform.exe. Your DAMA chapter can also keep track of meeting
attendance for the purpose of recertification and submit on a timely basis. There is an
annual maintenance fee to ICCP for keeping track of your recertification credits. You will
receive an annual transcript from the ICCP.
CDMP Contact Information
For Questions on the CDMP Certification Program:
Contact DAMA International ICCP Directors at:
ICCP_Director@dama.org or ICCP_Liaison@dama.org
To Order the DAMA Data Management Examination Guide:
DAMAi@dama.org
P.O. Box 5786
Bellevue, WA 98006-5786
415-562-2636
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The Data Management Examination Guide
The DAMA website is www.dama.org for further information and an application.
To Order the ICCP CDMP Study Guide or For Questions on CDMP Testing,
Administration and Recertification:
Contact the ICCP Office at:
847-299-4227 or 800-843-8227 (phone)
847-299-4280 (fax), or
office@iccp.org.
The ICCP website is www.iccp.org for further information and an application.
Page 11 of 122
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The Data Management Examination Guide
ICCP Data Management Specialty Examination Outline
How To Read The Data Management Subject Outline
In the examination outline, a consistent set of syntax conventions has been used:
•
•
•
•
Outline elements with numeric level leaders imply inclusivity. Concepts not
within the numbered structure will not be tested.
Outline elements with a “•” bullet leader are examples to clarify the content of a
numbered element, and are not necessarily inclusive.
Numbers in parentheses after an element name indicate the number of questions
in the exam, which will be presented on the subject indicated by the element name
and all subordinate elements. These allocations are guidelines established by the
Test Management Council, and are followed as closely as possible in selecting
questions for the exam. There are 100 multiple-choice questions on each exam
version and this outline reflects this total.
The characters “D#” after an element name indicate the target “depth” of
questions to be posed on the subject indicated by the element name and all
subordinate elements. The depths of knowledge are defined as follows:
D1
D2
D3
D4
D5
D6
Recognition
Knowing what a concept is called.
Differentiation Knowing the external differences between a concept and a
neighboring concept.
Description
Knowing the external characteristics of a concept.
Usage
Knowing how to use instances of the concept and why.
Structure
Knowing the internal structure of the concept — its
components and the relationships among these components.
Construction
Knowing how to put together instances of the concept
tailored to specific purposes.
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The Data Management Examination Guide
Data Management Exam Subject Outline
1.0. Data Management Function
(18) Section 1 Total
1.1 Planning
(8)
1.1.1.
•
•
•
1.1.2.
•
•
•
•
D4
Scope & Charter
Data Management customer base
Vision, goals, objectives
Functions / services
Data Management Plans
Strategic data management plan (linked to business plan)
Organizational structure plan and budgets
Metadata management budgets, metrics, audits
Data management oversight/control, e.g., data standards approval
committee, technology change management committee, data management
process change management committee, enterprise data management
‘board of directors’
• Enterprise data / information framework
• Data portfolio management plan
• Relationship management plan (vendor, customer, employee)
• Data quality management plan
• Data management process maturity improvement plan
• Data and data management configuration management plan
• Data and data management standards management plan
1.1.3. Policies / Standards / Processes / Procedures / Guidelines
• Internal to data management organization
• Customer data / metadata guidelines
1.2. Organization
1.2.1.
•
•
1.2.2.
•
•
•
(2)
D3
(8)
D3
Types of Staff Training
Orientation for new employees
Continuing education for required skills, retraining
Communication
Marketing data services and benefits
Customer education / training
Publishing newsletters and web site news
1.3. Roles & Responsibilities
1.3.1. Data Administration
• Data planning, policy development
• Data architecture
Page 13 of 122
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The Data Management Examination Guide
•
•
•
•
•
1.3.2.
•
•
•
•
1.3.3.
•
•
•
•
•
1.3.4.
•
•
•
1.3.5.
•
•
•
•
•
•
•
•
1.3.6
•
•
•
•
Data requirements modeling: conceptual (entity types and their
relationships), logical (ERA), and physical (database design)
Data model management
Data resource control and quality
Standards management, setting, communication and enforcement
Liaison with Database Administrators, Business Analysts, Management,
Users
Metadata Administration
Metadata planning, policy development
Metadata requirements gathering
Metamodeling (metadata modeling)
Metadata tool administration (metadata registries and repositories)
Database Administration
Definition and organization of physical database
Protection and recovery of physical database
Data archiving and deletion
Optimization and documentation of physical databases
Liaison with Data Administrators, Business Analysts, Management, Users
Data Warehouse Administration
Warehouse modeling, design, implementation, and operation
Operational data store modeling, design, implementation, and operation
Data access administration
Information Stewardship
Business information steward
Managerial information steward
Physical data trustee
Originator of business rules
Information producer
Data quality accountability
Metadata creation
Information usage and knowledge worker stewardship
Configuration Management
Database
Data models
Data standards
Metadata management tools
Page 14 of 122
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The Data Management Examination Guide
2.0 Data & Metadata Infrastructures Creation / Maintenance (15) Section 2 Total
2.1 Planning for Data & Metadata
2.1.1
•
•
•
•
•
•
•
•
2.1.2.
•
•
•
•
•
D4
Architectures
Enterprise Data
Data Sourcing
Data Distribution
Data Integration
Change Authorization
Zachman Framework
Data Processing Architectures (i.e. client-server, distributed data, etc.)
Metadata Architectures
Data Architecture Methods
Information Engineering
Enterprise Architecture Planning
Data Life Cycle
Data Reengineering
Prototyping
2.2.Tools and Technology Types
2.2.1.
•
•
•
2.2.2.
•
•
•
•
•
2.2.3.
•
•
•
(6)
(9)
Data
Database Management Systems (DBMS & ODBMS)
Data modeling tools
Extract, transform, and load (ETL) tools
Metadata & Descriptive Information
Data dictionaries
Data directories
Data encyclopaedias
Metadata registries (e.g. ISO/IEC 11179)
Metadata repositories
Data Issues
Business intelligence technologies (OLAP, Data Mining, etc.)
Data management and the Internet / Intranet
Data management and unstructured data
Page 15 of 122
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D3
The Data Management Examination Guide
3.0. Data Analysis and Modeling
3.1. Data / Metadata Analysis & Design
3.1.1.
•
•
•
•
3.1.2.
•
•
•
•
•
•
(6)
D5
Fact Finding Techniques
Interviewing
Surveys, questionnaires
JAD sessions
Legacy systems analysis
Requirements Definition and Management
Evaluation of current environment and documentation
Future state
Gap analysis
Business rules (discovery, validation and documentation)
Data / process matrices
Requirements tracking and management to implementation
3.2. Data Model Components
3.2.1.
•
•
•
•
•
•
•
•
•
•
3.2.2.
•
•
3.2.3.
•
•
•
•
3.2.4.
•
•
•
•
(37) Section 3 Total
(21)
Logical Data Model
Entity type
Relationship type
Attributes and their roles
Definitions
Key
Cardinality
Optionality
Metadata type
Rules: Business / data integrity
Normalization
Dimensional Warehouse
Fact
Dimension
Object Oriented / UML
Object
Class type
Attribute type
Relationship type
Data Representations in Process Models
Business views / presentation level
Trigger
Stored procedure
Object method
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D5
The Data Management Examination Guide
3.3. Data / Metadata Model Management
3.3.1.
•
•
•
•
•
•
•
•
3.3.2.
•
•
•
•
3.3.3.
•
•
•
•
•
•
•
(10)
D5
Types of Data Models
Conceptual
Logical
Physical
Data warehouse
Metamodels / meta-metamodels
Universal / industry models
Object class
Data life cycle
Scope of Model and Metadata
Enterprise wide
Business area
Project oriented
Subject area
Data Model Support
Creation
Maintenance
Version control
Comparison
Merging
Importing / exporting
Linkages and mappings between enterprise, logical, physical data models,
and process models
Page 17 of 122
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The Data Management Examination Guide
4.0.Data / Metadata Infrastructure Management
4.1. Standards, Policies, Procedures, Guidelines
(12) Section 4 Total
(7)
D5
4.1.1. Standards Management Process
• Awareness of external standards, e.g. ANSI and ISO/IEC data and data
management related standards
• Creation/identification
• Approval
• Enforcement
• Maintenance
4.1.2. Data Models
• Naming conventions for entities, relationships, attributes, etc.
• Business and data integrity rules
4.1.3. Data Elements
• Element types
• Naming conventions
• Metadata / definition principles
• Legacy element linkages
• Data element audit
4.2. Data Security and Privacy
(5)
4.2.1 Data Security Principles
• Accountability
• Authorization
• Availability
4.2.2. Data Security Policy Types
• Data stewardship and trustee responsibilities
• Data and instance value access sensitivities (e.g. privacy, corporate
confidentiality, data aggregation sensitivity issues)
• Trans-border data flow
• Data content
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D3
The Data Management Examination Guide
5.0. Information Quality Management
5.1. Information Quality Principles
(18) Section 5 Total
(6)
D3
5.1.1. Definition
• Data and Information
• Information quality
• Data definition as Information Product Specification
• Data definition quality
• Information architecture (data model) quality
• Business drivers
• Costs of nonquality information
5.1.2. Information Quality Characteristics
• Conformance to definition
• Completeness
• Validity
• Accuracy
• Precision
• Non duplication
• Consistency of redundant data
• Timeliness
• Usefulness
• Objectivity (of presentation)
• Presentation clarity
5.1.3 Data Definition (or Information Product Specification) Quality
Characteristics
• Properly formed name, in accordance with approved naming convention
standard
• Standard, single enterprise abbreviations for new development
• Name appropriate to knowledge workers
• Correct, clear, and complete definition
• Business term (used in data definition) defined in glossary
• Correctly specified value domain and definition (of code values)
• Properly defined data value type (not just alphanumeric, etc., but domain
type (corresponding to class words, e.g., data, code, amount, ID, etc.)
• Correct, complete, and useful business rule specification
5.2. Information Quality Assessment / Audit
(4)
5.2.1. Quality Assessment Characteristics
• Data definition quality assessment process/techniques
• Data model / requirements quality assessment process/techniques
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D4
The Data Management Examination Guide
5.2.2. Quality /Cost Measurement
• Baseline data cost calculation (Measurement of cost of redundancy and
interfaces)
• Cost of non quality information
• Value chain relationship between quality information and business drivers
5.3. Information Quality Improvement
(8)
5.3.1. Data Corrective Maintenance
• Data correction of defective data
• Redesign processes/systems producing poor quality data content or
presentation
5.3.2. Data Movement Control
• Mapping, transforming, data for data movement planning
• Quality audit and control of data movement
5.3.3. Information Quality Process Improvement
• Root Cause Analysis and Cause-and-Effect Diagrams
• Shewhart Cycle (Plan-Do-Check-Act) process for improvement
• Information defect prevention techniques
5.3.4. Information Quality Culture Transformation
• Employee training in information quality techniques
• Management accountability for information quality (managerial
information stewardship
• Information quality management maturity assessment
• Gap analysis
• Information quality performance measures
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D3
The Data Management Examination Guide
1.0 Data Management Function
Overview
The Data Management mission consists of goals and objectives that guide the creation,
use, management and deletion of data across enterprise. Data Managements needs to be
planned into overall Information Technology Strategy which is linked to the Business
plan. To support the functions and services related to Data Management within the
organization, a dedicated group of professionals with oversight committees need to be in
place e.g., data standards approval committee, technology change management
committee. An important aspect of data management oversight is to establish common
Policies, Standards Procedures and Guidelines for data ontology.
Support services of training and communication will further the knowledge and usage of
Data Management within an organization. When new employees are hired, orientation
sessions are required from the Data Management group to gain an understanding of the
data environment and the use of data to do their job. For individuals involved in the dayto-day management of the data, the training should be comprehensive and potentially
involve mentoring of the Data Policies, Standards, Procedures and Guidelines.
Communication of data services and updates through the use of newsletters assists in
keeping the organization up-to-date with the progress and accomplishments of the group.
To support the Data Management Function, major roles and responsibilities for managing
data need to be defined. There are no standards that define titles or team structure.
Typically, there is a Manager role for the group that is responsible for planning,
organizing and directing, plus may be a subject matter specialist. The job descriptions
outlined in this section are based on standard guidelines of job titles and responsibilities
based on experience.
Topics
Data Management Planning
Data Management Scope & Charter
Data Management Plans
Policies / Standards / Processes / Procedures / Guidelines
Data Management Organization
Types of Staff Training
Communication
Roles & Responsibilities
Data Administration
Metadata Administration
Database Administration
Data Warehouse Administration
Information Stewardship
Configuration Management
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Questions
1.1. Planning
1.1.1
Scope & Charter
1. What is the primary mission/vision of Data Management?
A. To facilitate the development, management, and use of the data resources as a
vital asset.
B. Committed to excellence.
C. Database maintenance and enhancement for production application systems
D. Data analysis and modeling for projects in planning or analysis
2. Which is not an objective of Data Management?
A. Maintain the physical integrity and efficiency of data resources.
B. Education about the benefits of and methods for enhancing data quality.
C. Provide the architecture and guidelines for documenting and implementing data
resources.
D. Provide a cost effective and robust document and content management
capabilities, workflow and business process management capabilities.
3. Which of the following is not a valid scope of Data Management Function?
A. Requirements analysis and modeling
B. Enterprise-wide data coordination, integration, stewardship and use
C. Data security and quality
D. Economies of scale in purchasing.
4. Which one of the following is not a typical type of service in a Data Management
function?
A. Support for warehouse initiatives.
B. Database maintenance and enhancement for production application systems
C. Database design for projects in development
D. Data analysis and modeling for projects in planning or analysis
1.1.2 Data Management Plans
5. The goal of the Data Management Plan is to describe the resources and process used
to ensure high quality data. The Data Management Plan is usually part of what overall
strategy?
A. Information Technology Strategy
B. Information Technology Infrastructure Strategy
C. Application Infrastructure Plan
D. Information Technology Architecture Plan
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6. A Data Management Plan is usually part of what overall strategy?
A. Information Technology Strategy
B. Information Technology Infrastructure Strategy
C. Application Infrastructure Plan
D. Information Technology Architecture Plan
7. Which one of the following is not a part of a Data Management Plan?
A. Describe the roles and resources of program staff.
B. Define future direction of data management activities in a work plan.
C. Implement facilities and tools for managing metadata resources.
D. Development of a quality management plan.
8. Which Committee is not an oversight committee regarding Data Management?
A. Data Standards Approval Committee
B. Data Management Process Change Management Committee
C. Enterprise Data Management Board of Directors
D. Project Change Committee
9. What is the purpose of conducting a metadata management audit?
A. To ensure metadata management controls have achieved intended results.
B. Provide an additional source of information for the budget.
C. To determine that decisions made in a timely fashion with appropriate
criteria/guidance that uses all necessary data/information from automated systems
and, if applicable, users.
D. To determine that all appropriate policies/procedures been developed,
disseminated, kept up to date, and test checks made to ensure compliance.
10. Benchmarking will provide comparative information. Which of the following is not
a result of benchmarking?
A. Efficiency and how well resources and services (outputs) are delivered
B. Quality of services and extent to which customers are satisfied (outcomes)
C. Measure for evaluating outcomes or the results of program activity compared to
its intended purpose and program objectives
D. Best practices
11. Which role is not typically involved in a data management oversight committee?
A. Program Director/Manager
B. Users
C. Database Administrator
D. Data Analysts
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12. Which one of the following is not a benefit of a strong data portfolio management
program?
A. Maximize value of IT data investments while minimizing the risk
B. Improve communication and alignment between technology and business.
C. Encourage reuse of data throughout the organization.
D. Allow planners to schedule resources more efficiently.
13. Which one of the following is least desirable benefit of Enterprise data / information
framework?
A. Provides enterprise-wide definitions of concepts and data.
B. Provides a scoping tool for new initiatives.
C. Reduces data redundancy by providing transparency as to the meaning of data
items
D. Encourages re-use and consistent data structures across the enterprise
14. What party would not be considered when creating a relationship management plan?
A. Vendor / Supplier
B. Customer
C. Employee
D. President
15. A Relationship Management Plan when dealing with vendors / suppliers should be
part of which overall strategy?
A. Procurement Strategy
B. Quality Management Strategy
C. Enterprise Architecture Strategy
D. IT Strategy
16. Which area does a Quality Management Plan does not address?
A. Quality policies and procedures.
B. Roles, responsibilities and authorities.
C. Description of quality system.
D. Meta-metamodel
17. Which one of the following is not true when describing Capability Maturity Model
Integration (CMMI)?
A. Model framework to assess data and process maturity.
B. Model framework to determine priorities.
C. Model framework to institute process and data improvement.
D. Defines six levels of process maturity.
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18. What level is organizations CMMI maturity, if the data management requirements
are not being met?
A. Level 0
B. Level 1
C. Level 2
D. Level 3
19. What level is organizations CMMI maturity, if the data management requirements
are being met?
A. Level 0
B. Level 1
C. Level 2
D. Level 3
20. What level is organizations CMMI maturity, if the data management requirements
are being managed and tracked?
A. Level 1
B. Level 2
C. Level 3
D. Level 4
21. What level is organizations CMMI maturity, if the data management requirements
meet EIA Standard 859 Industry Standard for Data Management that includes nine high
level Data Management Principles?
A. Level 1
B. Level 2
C. Level 3
D. Level 4
22. Which one is not the purpose of the data management configuration management
plan?
A. Identify and describe the overall policies and methods for Configuration
Management.
B. Establish and provide the basis for a uniform and concise Configuration
Management practice
C. Manage the data for its entire lifecycle.
D. Retain data commensurate with value.
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23. Who is responsible for developing and implementing Data-Management planning for
projects, for ensuring that the activities are completed according to agreed standards and
timelines and for coordinating ongoing data management to support the business?
A. Data Manager
B. Data Analyst
C. Database Administrator
D. Business Manager
1.1.3 Policies / Standards / Processes / Procedures / Guidelines
24. What is the following statement: Data archives must include easily accessible
information about the data holdings, including quality assessments, supporting ancillary
information, and guidance and aids for locating and obtaining the data?
A. Policy
B. Standard
C. Procedure
D. Guideline
25. What is the following statement: Contact Information offers data groupings that are
used to describe a point of contact, address, and communication information?
A. Policy
B. Standard
C. Procedure
D. Guideline
26. What is the following statement: To keep the hard drives from getting full, please
back-up your data. 1. Put CD data you want to back up in one folder. The name of the
folder is the name of the CD. 2. Start the "Backup" program. 3. Click on "Data." 4. Move
the data you want to back up in "Data window." 5. Click on "Done." 6. Put the CD in the
CD-R and close the door. 7. Write the CD.
A. Policy
B. Standard
C. Procedure
D. Guideline
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27. What is the following statement: Aggregation of data values is appropriate for fields
with a large numbers of values, such as dates, age, and geographic areas; it is the primary
method used to collapse a dataset in order to create tables with no small numbers as
denominators or numerators in cells?
A. Policy
B. Standard
C. Procedure
D. Guideline
28. What is the following statement: Data custodians are responsible for creating and
maintaining metadata for their datasets?
A. Policy
B. Standard
C. Procedure
D. Guideline
29. Which of the following is the best answer for the definition of cost when following
the metadata procedure, to “state what the concept is, not only what it is not”.
A. Total spent for goods or services including money and time and labor.
B. Cost is a price paid.
C. Costs, which are not related to external costs.
D. Direct cost to the business owner of those items, which will be sold to customers.
1.2.
Organization
1.2.1. Types of Staff Training
30. Which one of the following is not appropriate for an orientation of the data
environment for new employees?
A. Acronym list.
B. Customer Service Policy.
C. Data Policy and Procedure.
D. WWW Design and Programming.
31. When embarking on continuing education for required skills or retraining, which
training method is least desirable?
A. Mentoring with another employee.
B. Workshops and seminars.
C. Classroom or computer based courses.
D. Booklets and information sheets.
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1.2.2. Communication
32. Which one of the following is the least beneficial when promoting data services?
A. Communicating data services and benefits.
B. Publishing newsletters and web site news.
C. Customer education and training.
D. Convening a meeting of the Organizations Management Team.
1.3.
1.3.1.
Roles & Responsibilities
Data Administration
33. Who is responsible for identifying and analyzing information needs for the enterprise
or business area, and develops and maintains data architecture?
A. Data Administrator
B. Manager, Data Administration
C. Data Administration Consultant
D. Database Administrator
34. Which one is not a responsibility of the Data Administrator?
A. Identify and analyze customer information needs.
B. Develop and maintain data architecture.
C. Develop and maintain strategic data plan.
D. Provide approval authority over metadata policies and design.
35. When hiring a Data Administrator which skill is the least preferred?
A. Relational Database experience.
B. Logical and Physical Data Modeling.
C. Project Management experience.
D. Strong written and oral communication skills.
36. Which role would a Data Administrator not typically interact?
A. Business Analyst
B. CEO
C. Repository Administrator
D. Management
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37. Who is responsible for planning, organizing, directing and controlling data
definition, data use, and ensure data availability for the enterprise?
A. Data Administrator
B. Manager, Data Administration
C. Metadata Administrator
D. Database Administrator
1.3.2. Metadata Administration
38. Who is responsible for creating, administrating and enforcing of standards,
guidelines and procedures for the use of metadata?
A. Data Administrator
B. Manager, Data Administration
C. Metadata Administrator
D. Database Administrator
39. Which is not a responsibility of the Metadata Administrator role?
A. Establish and maintain the metadata architecture.
B. Provide approval authority over metadata policies and design.
C. Maintain repository security profiles.
D. Provide final review and approval authority over data design for an application
system.
40. In a company with a Metadata team, which role would collect the requirements and
design the metadata solution?
A. Metadata Administrator.
B. Manager, Metadata Administration.
C. Metadata Analyst.
D. Metamodelers.
1.3.3. Database Administration
41. Which responsibility is not typically a responsibility of the Database Administrator?
A. Establish and maintain sound backup and recovery policies and procedures.
B. Implement and maintain database security (create and maintain users and roles,
assign privileges).
C. Perform database tuning and performance monitoring.
D. Perform application tuning and performance monitoring.
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42. Which role would a Database Administrator not typically interact?
A. Business Analyst
B. CEO
C. Data Administrator
D. Management
43. When hiring a Database Administrator which skill is the least preferred?
A. Relational Database, related utilities and tools experience.
B. Physical Data Modeling.
C. Ability to perform both Relational Database and Operating System performance
tuning and monitoring.
D. Network security administration.
44. Which role in an organization would develop the referential integrity constraint
scripts?
A. Data Administrator
B. Manager, Data Administration
C. Data Analyst
D. Database Administrator
45. Who has the responsibility to recover the physical database in the event of a power
disruption?
A. Data Administrator
B. Manager, Data Administration
C. Data Analyst
D. Database Administrator
1.3.4 Data Warehouse Administration
46. Which responsibility is not typically a responsibility of the Data Warehouse
Administrator?
A. Data Warehouse data modeling and design.
B. Data Warehouse implementation and refresh.
C. Data Access administration.
D. Installing the Operating System on the Data Warehouse server.
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47. When hiring a Data Warehouse Administrator which skill is the least preferred?
A. Relational Database, related utilities and tools experience.
B. Ability to calculate Data Warehouse return on investment, costs and benefits.
C. Expert in data structure including parallel data structure.
D. Logical and Physical Data Modeling.
1.3.5. Information Stewardship
48. Which role would review and approve data definitions and domain value
specifications for business data?
A. Business Information Steward
B. Managerial Information Steward
C. Physical Data Trustee
D. Information Producer
49. Who has authority to select and mandate Business Information Stewards?
A. Business Information Steward
B. Managerial Information Steward
C. Physical Data Trustee
D. Information Producer
50. Which role is not responsible for data quality?
A. Business Information Steward or Managerial Information Steward
B. Physical Data Trustee
C. Information Producer
D. Everyone is responsible for data quality.
51. Which one is not a responsibility of the Physical data trustee?
A. Creation of data standards.
B. Enforcement of physical security.
C. Performance tuning of physical databases.
D. Backup and Recovery of physical databases.
52. Who should be responsible for creating the data dictionary entries?
A. Repository Administrator
B. End User
C. Data Modeler
D. Data Librarian
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1.3.6
Configuration Management
53. A new data model is created and rolled into Production. Which process is
responsible for registering the modification in the Configuration Management Database
(CMDB)?
A. Change Management
B. Configuration Management
C. Problem Management
D. Release Management
54. Which of the following is a Configuration Item (CI)?
A. Organization Structure
B. Data Model
C. An incident
D. A process
55. Which one is not a discipline of Data Management Configuration?
A. Status Accounting
B. Collection
C. Approval
D. Distribution
56. Which item is not a responsibility of the Configuration and Data Management team?
A. Management of all documentation and specifications.
B. Configuration and data management of programs.
C. Maintaining requirements of deliverables through the data change process.
D. Providing storage, retrieval, distribution, and management of program data.
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Quick Answers
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Detailed Answers
1. Answer: A. To facilitate the development, management, and use of the data resources
as a vital asset. The primary mission of Data Management is to facilitate the
development, management, and use of the data resources as a vital asset. The services of
Data Management comprise of the following: Database maintenance and enhancement
for production application systems and Data analysis and modeling for projects in
planning or analysis.
2. Answer: D. Provide a cost effective and robust document and content management
capabilities, workflow and business process management capabilities. Objectives of Data
Management are: Maintain the physical integrity and efficiency of data resources;
education about the benefits of and methods for enhancing data quality; and provide the
architecture and guidelines for documenting and implementing data resources.
3. Answer: D. Economies of scale in purchasing. The scope of Data Management
function include: Requirements analysis and modeling; Enterprise-wide data
coordination, integration, stewardship and use; and data security and quality.
4. Answer: A. Support for warehouse initiatives. Data Management services include data
maintenance and enhancement for production application systems; Database design for
projects in development; and Data analysis and modeling for projects in planning or
analysis.
5. Answer: A. Information Technology Strategy. A Data Management Plan is usually
part of the overall Information Technology Strategy. The Information Technology
Strategy leads to Infrastructure Strategy, Information Technology Architecture Plan and
Application Infrastructure Plan.
6. Answer: A. Information Technology Strategy. A Data Management Plan is usually
part of the overall Information Technology Strategy. The Information Technology
Strategy leads to Infrastructure Strategy, Information Technology Architecture Plan and
Application Infrastructure Plan.
7. Answer: C. Implement facilities and tools for managing metadata resources. Data
Management Plans are high level and describe the roles and resources of program staff,
define future direction of data management activities in a work plan and the development
of a quality management plan.
8. Answer: D. Project Change Committee. Data Management oversight committees have
been called: Data Standards Approval Committee; Data Management Process Change
Management Committee; and Enterprise Data Management Board of Directors. Project
Change Committee refers to changes made to project scope, time, or cost.
9. Answer: A. To ensure metadata management controls have achieved intended results.
The purpose of conducting a metadata management audit is to ensure metadata
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management controls have achieved intended results as systematic and proactive
measures. Conducting a Metadata Management Control Review assists managers in
developing and implementing appropriate, cost-effective management controls for
results-oriented metadata management; assessing the adequacy of metadata management
controls in programs and operations; identify needed improvements; take corresponding
corrective action; and report on the status of metadata management control
improvements.
10. Answer: D. Best practices. The results of benchmarking will measure: efficiency
and how well resources and services (outputs) are delivered; quality of services and
extent to which customers are satisfied (outcomes); and measure for evaluating outcomes
or the results of program activity compared to its intended purpose and program
objectives. Only one benchmark will not provide comparative information. After
measuring over time, will benchmarks will provide comparative information.
11. Answer: C. Database Administrator. Typically the data management oversight
committee comprises of Program Director/Manager, Users, and Data Analysts. A
Database Administrator is not typically part of the data management oversight
committee.
12. Answer: D. Allow planners to schedule resources more efficiently. A strong data
portfolio management program
13. Answer: B. Provides a scoping tool for new initiatives. The least desirable benefit of
Enterprise data / information framework is provides a scoping tool for new initiatives.
New initiatives are first scoped by the business needs to gain the requirements.
Enterprise data / information framework: Provides an enterprise-wide definitions of
concepts and data; Reduces data redundancy by providing transparency as to the meaning
of data items; and Encourages re-use and consistent data structures across the enterprise.
14. Answer: D. President. When creating a relationship management plan, the vendor,
customer and employee should be considered. A relationship management plan should
be approved for each project and the goal is to improve consistency in the way we
approach relationships.
15. Answer: A. Procurement Strategy. A Relationship Management Plan when dealing
with vendors / suppliers should be part of the Procurement Strategy. The Procurement
Strategy should address when and how potential suppliers are to be engaged in the
development of Relationship Management Plans.
16. Answer: D. Meta-metamodel. The Quality Management Plan addresses: quality
policies and procedures; roles, responsibilities and authorities; and description of quality
system.
17. Answer: D. Defines six levels of process maturity. The Capability Maturity Model
defines five levels of process maturity; Model framework to assess data and process
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maturity; Model framework to determine priorities; and Model framework to institute
process and data improvement.
18. Answer: A. Level 0. If the data management requirements are not being met, the
organization is at CMMI maturity level 0.
CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3
Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured
Predictable Process); 5 – Optimizing (Continuously Improving Process).
19. Answer: B. Level 1. If the data management requirements are being met, the
organization is at CMMI maturity level 1. CMMI Levels: 0 – not performed, 1 – initial, 2
– managed (disciplined process); 3 Defined (Standard, consistent process); 4 –
Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously
Improving Process).
20. Answer: B. Level 2. If the data management requirements are being met, the
organization is at CMMI maturity level 2. CMMI Levels: 0 – not performed, 1 – initial, 2
– managed (disciplined process); 3 Defined (Standard, consistent process); 4 –
Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously
Improving Process).
21. Answer: C. Level 3. If the data management requirements meet EIA standard 859
Industry Standard for Data Management, the organization is at CMMI maturity level 3.
CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3
Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured
Predictable Process); 5 – Optimizing (Continuously Improving Process). EIA standard
859 includes nine high level Data Management Principles. The principles address
functions of Data Management:
1. Define the organizationally relevant scope of Data Management.
2. Plan for, acquire, and provide data responsive to customer requirements.
3. Develop DM processes to fit the context and business environment in which they will
be performed.
4. Identify data products and views so their requirements and attributes can be
controlled.
5. Control data repositories, data products, data views, and metadata using approved
change control process.
6. Establish and maintain an identification process for intellectual property, proprietary,
and competition-sensitive data.
7. Retain data commensurate with value.
8. Continuously improve data management.
9. Effectively integrate data management and knowledge management.
22. Answer: D. Retain data commensurate with value. The purpose of the data
management configuration management plan is to identify and describe the overall
policies and methods for Configuration Management; Establish and provide the basis for
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a uniform and concise Configuration Management practice; and Manage the data for its
entire lifecycle.
23. Answer: A. Data Manager. A data manager is responsible for developing and
implementing Data-Management planning for projects, for ensuring that the activities are
completed according to agreed standards and timelines and for coordinating ongoing data
management to support the business, known as the data management standards
management plan.
24. Answer: A. Policy. The sentence is a Policy statement: Data archives must include
easily accessible information about the data holdings, including quality assessments,
supporting ancillary information, and guidance and aids for locating and obtaining the
data. A Policy is a prescribed or proscribed course of action or behavior, which is to be
followed with respect to the acquisition, deployment, implementation or use of
information technology resources. It is not a standard, as it does not outline a specific
technical approach. It is not a procedure, as it does not offer a set of administrative
instructions for implementation of a policy or standard. It is not guideline that should
offer a detailed plan or explanation to guide you in setting standards or determining a
course of action.
25. Answer: B. Standard. The sentence is a Standard statement: Contact Information
offers data groupings that are used to describe a point of contact, address, and
communication information. Standard(s) is a prescribed or proscribed specific technical
approach, solution, methodology, product or protocol which must be adhered to in the
design, development, implementation or upgrade of data architecture. Standards are
intended to establish uniformity in data. Standards should be designated as either
"preferred" or "mandatory". It is not a procedure, as it does not offer a set of
administrative instructions for implementation of a policy or standard. It is not a
guideline, which should offer a detailed plan or explanation to guide you in setting
standards or determining a course of action.
26. Answer: C. Procedure. The sentence is a Procedure statement: To keep the hard
drives from getting full, please back-up your data. Procedure is a set of administrative
instructions for implementation of a policy or standard. It is not a guideline, which should
offer a detailed plan or explanation to guide you in setting standards or determining a
course of action.
27. Answer: D. Guideline. The sentence is a Guideline statement: Aggregation of data
values is appropriate for fields with a large numbers of values, such as dates, age, and
geographic areas; it is the primary method used to collapse a dataset in order to create
tables with no small numbers as denominators or numerators in cells. A guideline offers
a detailed plan or explanation to guide you in setting standards or determining a course of
action.
28. Answer: A. Policy. The sentence is a Metadata Policy statement: Data custodians are
responsible for creating and maintaining metadata for their datasets A Policy is a
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prescribed or proscribed course of action or behavior, which is to be followed with
respect to the acquisition, deployment, implementation or use of information technology
resources. It is not a standard, as it does not outline a specific technical approach. It is not
a procedure, as it does not offer a set of administrative instructions for implementation of
a policy or standard. It is not guideline that should offer a detailed plan or explanation to
guide you in setting standards or determining a course of action.
29. Answer: A. Total spent for goods or services including money and time and labor.
When creating metadata definitions a guideline to put in place according to ISO/IEC
11179, is to “state what the concept is, not only what it is not”.
30. Answer: D. WWW Design and Programming. When creating an orientation of the
data environment for new employees it is appropriate to have items like: Acronym list,
Customer Service Policy, Data Policy and Procedure, Data Standards, Data Guidelines,
IT Strategy, and potentially even Data Models where appropriate. WWW Design and
Programming would be most beneficial to Analyst and Programmers.
31. Answer: D. Booklets and information sheets. When embarking on continuing
education for required skills or retraining the least desirable method of training is
booklets and information sheets due to the non-interactiveness of the information.
32. Answer: D. Convening a meeting of the Organizations Management Team. When
promoting data services, the goal is to publicize the efforts to the Organizations data
customers. Convening a meeting of the Organization Management Team only reaches a
limited audience and they may not be suitable people. Using a wide variety of marketing
and communication vehicles will assist in targeting the message like: Marketing data
services and benefits; Customer education / training; and Publishing newsletters and web
site news.
33. Answer: A. Data Administrator. The Data Administrator identifies and analyzes
information needs for the enterprise or business area, and develops and maintains data
architecture plus the strategic data plan. The Data Administrator provides project support
for data processing applications like data modeling and designing physical database. The
Metadata Administrator is responsible for creating, administrating and enforcing of
standards, guidelines and procedures for the use of metadata plus metadata query and
analysis tools. The Manager, Data Administration is responsible for planning,
organizing, directing and controlling data definition, data use, and ensuring data
availability for the enterprise.
34. Answer: D. Provide approval authority over metadata policies and design. The Data
Administrator identifies and analyzes information needs for the enterprise or business
area, and develops and maintains data architecture plus the strategic data plan. The Data
Administrator provides project support for data processing applications like data
modeling and designing physical database. A Metadata Administrator would provide
approval authority over repository policies and design. A Metadata Administrator would
work with the Data Administrator. Additional responsibilities for the Data Administrator
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include developing and enforcing standards for metadata through the review of
definitions. Also, assist in developing procedures and data management policies that
ensure the integrity, consistency and control of the enterprise's data resource.
35. Answer: C. Project Management experience. When hiring a Data Administrator the
skill that may be desirable but least preferred in the above list is Project Management
experience. Typically, skills for Data Administrators are: Relational Database
experience, logical and physical data modeling, strong written and oral communication
skills, strong analysis skills and prior work experience.
36. Answer: B. CEO. The Data Administrator would not typically interact with the CEO
in an organization. The Data Administrator would interact with Business Analysts,
Repository Administrator, Management and Users/Customers of the data.
37. Answer: B. Manager, Data Administration. The Manager, Data Administration is
responsible for planning, organizing, directing and controlling data definition, data use,
and ensure data availability for the enterprise. The Data Administrator identifies and
analyzes information needs for the enterprise or business area, and develops and
maintains data architecture plus the strategic data plan. The Metadata Administrator is
responsible for creating, administrating and enforcing of standards, guidelines and
procedures for the use of metadata plus metadata query and analysis tools.
38. Answer: C. Metadata Administrator. The Metadata Administrator is responsible for
creating, administrating and enforcing of standards, guidelines and procedures for the use
of metadata plus metadata query and analysis tools. The Manager, Data Administration
is responsible for planning, organizing, directing and controlling data definition, data use,
and ensure data availability for the enterprise. A Database Administrator conducts data
store modeling, design, implementation, and operation.
39. Answer: D. Provide final review and approval authority over data design for an
application system. The Metadata Administrator role would: Establish and maintain the
metadata architecture; Provide approval authority over metadata policies and design; and
Maintain repository security profiles in addition to Metadata tool administration.
40. Answer: C. Metadata Analyst. In a company with a Metadata team, the Metadata
Analyst would collect the requirements and design the metadata solution. The
Metamodeler would convert the requirements into metamodels. The Metadata
Administrator is responsible for creating, administrating and enforcing of standards,
guidelines and procedures for the use of metadata plus metadata query and analysis tools.
The Manager, Data Administration is responsible for planning, organizing, directing and
controlling data definition, data use, and ensuring data availability for the enterprise.
41. Answer: D. Perform application tuning and performance monitoring. A Database
Administrator would be responsible for: Establish and maintain sound backup and
recovery policies and procedures; Implement and maintain database security (create and
maintain users and roles, assign privileges); Perform database tuning and performance
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monitoring; Capacity planning; Perform general technical trouble shooting and give
consultation to development teams.
42. Answer: B. CEO. The Database Administrator would not typically interact with the
CEO in an organization. The Database Administrator would interact with Business
Analysts, Data Administrator, Management and Users/Customers of the data.
43. Answer: D. Network security administration. When hiring a Database Administrator
the skill that may be desirable but least preferred in the above list is Network security
administration experience. Typically, skills for Database Administrators are: Relational
Database; related utilities and tools experience, physical data modeling; ability to perform
both Relational Database and Operating System performance tuning and monitoring; and
prior work experience.
44. Answer: D. Database Administrator. In an organization, the Database Administrator
would develop the referential integrity constraint scripts. The Data Analyst would work
with the Database Administrator to link the logical to physical data model.
45. Answer: D. Database Administrator. In an organization, the Database Administrator
would have the responsibility to recover the physical database in the event of a power
disruption.
46. Answer: D. Installing the Operating System on the Data Warehouse server. A Data
Warehouse Administrator would be responsible for: Data Warehouse data modeling and
design; Data Warehouse implementation and refresh; Data Access administration;
Perform application performance monitoring; Perform general technical trouble shooting
and give consultation to development and metadata teams.
47. Answer: B. Ability to calculate Data Warehouse return on investment, costs and
benefits. When hiring a Data Warehouse Administrator the skill that may be desirable
but least preferred in the above list is the ability to calculate Data Warehouse return on
investment, costs and benefits. Typically, skills for Data Warehouse Administrators are:
Relational Database; related utilities and tools experience, logical and physical data
modeling; Expert in data structure including parallel data structure; Extract Transform
and Load tool experience; and prior work experience.
48. Answer: A. Business Information Steward. Business Information Steward would
review and approve data definitions and domain value specifications for business data.
Other responsibilities would include: validating business rules and keeping the domain
values current across the Enterprise. Managerial Information Steward is responsible for
setting information policy and creating information measures for either the organization
or a specific department or business area. Physical Data Trustee is accountable for the
integrity of the physical data assets. An Information Producer is accountable for the
content of the information.
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49. Answer: B. Managerial Information Steward. The Managerial Information Steward
has the authority to select and mandate Business Information Stewards. Physical Data
Trustee is accountable for the integrity of the physical data assets. An Information
Producer is accountable for the content of the information.
50. Answer: D. Everyone is responsible for data quality. Everyone in an organization is
responsible for data quality. While some roles, like a CEO may not be involved in data
entry or usage of transactional systems, they would offer support both financial and
policy.
51. Answer: A. Creation of data standards. The Physical data trustees are responsible for
enforcement of physical security, performance tuning of physical databases, and backup
and recovery of physical databases.
52. Answer: B. End User. The End User would be responsible for creating the data
dictionary entries. The Repository Administrator would set up the structure and
implement the data dictionary. The Data Librarian would assist in cataloguing and
categorizing the data.
53. Answer: B. Configuration Management. A new data model is created and rolled into
Production. The process that is responsible for registering the modification in the
Configuration Management Database (CMDB) is Configuration Management.
Configuration and Data Management organizations are responsible for defining,
controlling, integrating and implementing essential policies and procedures that provide
Configuration Management (CM) and Data Management (DM) discipline on Programs
and contracts. Configuration Management Database is a database, which contains all
relevant details of each Configuration Item (CI) and details of the important relationships
between CIs.
54. Answer: B. Data Model. An example of a Configuration Item is data model. A
Configuration item is a component of an infrastructure, or an item associated with
infrastructure that needs to be managed and controlled by Configuration Management.
55. Answer: A. Status Accounting. The disciplines of Data Management Configuration
are: Planning, Collection, Approval, Distribution, Storage, and Retrieval of data that are
implemented through standard procedures and address specific customer or contractual
data management requirements.
56. Answer: A. Management of all documentation and specifications. The only
documents that should be managed through Configuration Management are those that
relate to: hardware, software, firmware, data model, documentation, test, test fixtures and
test documentation of an automated information system, throughout the life of a system.
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2.0 Data & Metadata Infrastructures Creation / Maintenance
Overview
An overall planning function needs to occur to govern the creation and maintenance of
data and metadata infrastructures. The scope of the data and metadata infrastructure
includes the definition of all activities and processes involved in the definition, creation,
formatting, storage, access and maintenance of data and metadata. Data Architecture
Methods defines the process to create infrastructure like using Information Engineering.
Information Engineering has many purposes, including organization planning, business
re-engineering, application development, information systems planning and systems reengineering.
There are a number of data and metadata tools that perform various tasks associated with
managing and deploying Data Management that provide flexibility needed to support the
activities and processes defined in the infrastructure. Data Management tools include
Database Management Systems (relational and object), Data modeling tools and Extract,
transform, and load (ETL) tools. Metadata tools include Data dictionaries, Data
directories, Data encyclopaedias, Metadata registries (e.g. ISO/IEC 11179) and Metadata
repositories. The ISO/IEC 11179 Information Technology: Metadata Registries (MDR)
specification developed by the ISO (International Standards Organization) and the IEC
(the International Electrotechnical Commission) defines a number of fields and
relationships for Metadata Registries including a detailed metamodel for defining and
registering administered items, of which the primary component is a Data Element. Each
data and metadata tool has a different purpose and usage.
Topics
Data Architecture Methods
Architectures
Data Architecture Methods
Tools and Technology Types
Data
.
Metadata & Descriptive Information
.
Data Issues
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Questions
2.1
Planning for Data & Metadata
2.1.1
Architectures
1. Which of the following is not a valid benefit of Enterprise Data Architecture?
A. Organizes data around the enterprise's data subjects to create a shared data
resource.
B. Integrated view of enterprise data.
C. Economies of scale in purchasing Case tools.
D. Enables organizational change.
2. What is the best answer to why would Enterprise Data Architecture be created?
A. Enterprise Data Architecture can be created in one iteration.
B. Diagram application-specific databases.
C. Information is an asset of the entire organization.
D. Design stability and data object abstraction and generalization.
3. Which of the following is not a reason to architect Source Data?
A. Determine sources of data needed.
B. Determine the index for the data mart.
C. Diagram application-specific source data for extraction.
D. Determine methods for extraction and delivery.
4. Which one of the following is not a goal of Source Data Architecture?
A. Ensure that the source data is extracted only once.
B. Define the scope and implementation of the data warehouse.
C. Oversee the construction of the enterprise data warehouse.
D. Determine the monthly flat file transmission protocol.
5. Which one of the following is targeted towards the efficient delivery of the proper
information to the proper recipients?
A. Data Sourcing
B. Data Distribution
C. Data Integration
D. Enterprise Data
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6. Which one of the following requires combining and matching information from
different sources, and resolving a variety of conflicts?
A. Data Sourcing
B. Data Distribution
C. Data Integration
D. Enterprise Data
7. What is a fundamental principle in Change Authorization of Architectures?
A. Single point of authorization.
B. Single point of access.
C. Private key encryption on data.
D. Standard for communication.
8. Which of the following is not a feature of the client in client-server architecture?
A. Passive
B. Active
C. Sending requests
D. Waits until reply arrives
9. Which of the following is not a feature of the server in client-server architecture?
A. Passive
B. Active
C. Waiting for requests
D. On requests serves them and send a reply
10. What is the best name for a network called if the networks consists clients,
application servers which process data for the clients, and database servers, which store
data for the application servers?
A. 2-tier Architecture
B. 3-tier Architecture
C. n-tier Architecture
D. Multi-tier Architecture
11. Which one of the following is not a source of metadata?
A. Case Tools
B. Applications
C. Physical Database
D. Company Directory
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12. Which type of analysis is needed when beginning Metadata solution architecture?
A. Metadata record
B. Metadata flows
C. Metadata Categorization
D. Metadata user
13. What is best definition of the Zachman Framework?
A. A 36-cell Matrix.
B. A Normalized schema.
C. A good analytical tool.
D. Specific to methods/tools.
2.1.2. Data Architecture Methods
14. Which one is not a benefit of Enterprise Architecture Planning?
A. Consistency and compatibility of systems.
B. Interoperability between systems and databases.
C. Economies of scale in purchasing and developing systems.
D. Greater accounting staff effectiveness.
15. Which one of the following does Enterprise Architecture Planning does not address?
A. Data management
B. Application environment and development toolsets
C. Maintain a secure infrastructure and IT support for networks and distributed
systems
D. Middle-ware and transaction management
16. An Enterprise Architecture Plan is usually part of what overall strategy?
A. Information Technology Strategy
B. Information Technology Infrastructure Strategy
C. Application Infrastructure Plan
D. Information Technology Architecture Plan
17. Which one of the following phases is not part of the data life cycle?
A. Create/Store
B. Modify/Update
C. Delete
D. Shred
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18. What is the definition of data life cycle management?
A. Data Life cycle management is a product.
B. Data Life cycle management is an approach to managing an organization's data.
C. Data Life cycle describes the CRUD matrix of data elements.
D. Data Life cycle is the storage used to store active and inactive data.
19. What is defined as "An integrated and evolutionary set of tasks and techniques that
enhance business communication throughout an enterprise enabling it to develop people,
procedures and systems to achieve its vision".
A. Information Engineering
B. Enterprise Architecture Planning
C. Data Reengineering
D. Prototyping
20. Which one is not a purpose of Information Engineering?
A. Organization planning.
B. Business re-engineering.
C. Application development.
D. Data Warehousing.
21. In which situation would a data reengineering apply?
A. Developing a migration strategy from one application environment to another.
B. Determining active and inactive data.
C. Enhance business communication throughout the enterprise.
D. Assist in developing the information management strategy.
22. What is the process of quickly putting together a working model in order to test
various aspects of the design, illustrate ideas or features and gather early user feedback?
A. Information Engineering
B. Enterprise Architecture Planning
C. Data Reengineering
D. Prototyping
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2.2.
Tools and Technology Types
2.2.1. Data
23. What is the definition of a Database Management System (DBMS)?
A. Controls the organization, storage and retrieval of data in a database.
B. A modeling language to define the schema.
C. Inverted list management.
D. Supports the database query language to interactively access data.
24. Which one is not a common DBMS model?
A. Hierarchical
B. Network
C. Relational
D. File
25. Which one is not a function of a DBMS?
A. A modeling language to define the schema
B. A database query language
C. Transaction method that ensures Atomicity, Consistency, Isolation, and Durability
(ACID)
D. RAID Disk arrays.
26. What is the definition of an Object Database Management System (ODBMS)?
A. Controls the organization, storage and retrieval of data in a database.
B. A modeling language to define the schema
C. Inverted list management
D. Supports the database query language to interactively access data.
27. Which one is not a function of an ODBMS?
A. Object Definition Language (ODL)
B. Object Query Language (OQL)
C. C++ and Java Binding.
D. Structured Query Language (SQL).
28. Which function does Extract, Transform, and Load (ETL) tool does not involve in
the process in data warehousing?
A. Extracting data from data sources.
B. Transforming data to fit business requirements.
C. Transforming metadata to fit business requirements.
D. Loading data into the data warehouse.
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29. The first part of an ETL process is to extract the data from what type of common data
source formats?
A. Relational database
B. Flat File
C. IMS
D. C++
30. Which one is not a typical function of the transformation process in ETL tools?
A. Translating code values
B. Deriving new calculated values
C. Joining or merging data from multiple sources
D. DDL SQL statements with SQL variations
31. Which of the following is not a type of load function of an ETL tool in the data
warehouse?
A. Overwrite old information
B. Insert new records
C. Update old record and Insert new record
D. Insert audit trail records.
32. Which of the following is not true of a Data Modeling Tool?
A. Specific to a DBMS
B. Produce a diagram summarizing the results of your data modeling efforts
C. Generate a database schema from a model.
D. Diagram of referential integrity constraints.
2.2.2. Metadata & Descriptive Information
33. What is the definition of a data dictionary?
A. Set of cleansed, organized, and transaction level data.
B. Database that tracks data element definitions.
C. Instances of characters.
D. Internal database that store information tracked, developed, and maintained by a
predefined set of single-vendor tools.
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34. What is the definition of a data dictionary?
A. An entity in a file system that contains a group of files.
B. LDAP directory services are examples of general-purpose distributed hierarchical
object-oriented directory technologies.
C. A repository or database of information.
D. Directory technology is often used in white page applications and network
information services.
35. What is the definition of a data encyclopaedia?
A. Set of cleansed, organized, and transaction level data.
B. Database that tracks data element definitions.
C. Instances of characters.
D. Internal database that store information tracked, developed, and maintained by a
predefined set of single-vendor tools.
36. What data is not typically held in a data registry?
A. Standardized information in a pre-defined model.
B. Metadata, system metadata, system engineering
C. Reference information
D. XML
37. Which one of the following is not a federated Service-Oriented Architecture (SOA)
standards-based registry?
A. UDDI registry without a repository.
B. UDDI registry with a proprietary repository
C. ebXML registry-repository
D. Combination of UDDI registry and ebXML registry-repository
38. What is defined as an automated resource “used to describe, document, protect,
control and access informational representations of an enterprise”.
A. Data dictionaries
B. Data directories
C. Metadata registry
D. Metadata repositories
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39. What is “an integrated, virtual holding area with vendor-independent input, access,
and structure; used to directly store metadata and/or metadata-based gateways to external
metadata”?
A. Data dictionaries
B. Data directories
C. Data encyclopaedias
D. Metadata repositories
2.2.3. Data Issues
40. What is the goal of business intelligence tools?
A. Detect patterns in data that explain the present and predict the future.
B. Visually representing the data.
C. Interactive “online” data exploration
D. “Slice-and-dice” analysis
41. Which one of the following is not a new way of interacting with data in extended
OLAP models?
A. Pivot tables
B. Small Multiples
C. Geospatial Analysis
D. Predictive Analytics
42. Which one of the following does not data mining tools have issues in analyzing?
A. E-mails.
B. Memos
C. Marketing material
D. Spreadsheet
43. What is a technique to increase searching of unstructured data?
A. Data Semantics
B. Data Ontologies
C. Classification and Taxonomy
D. Classes and Relations
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Detailed Answers
1. Answer: C. Economies of scale in purchasing Case tools. The benefits of an Enterprise
Data Architecture are that it organizes data around the enterprise’s data subject to create
shared data resources, an integrated view of enterprise data that enables organizational
change.
2. Answer: D. Design stability and data object abstraction and generalization. An
Enterprise Data Architecture should be created for design stability and data object
abstraction and generalization. Enterprise Data Architecture treats data like an
information asset; it is not application specific but enterprise specific. Enterprise Data
Architecture is typically created in more than one iteration.
3. Answer: B. Determine the index for the data mart. The source data should be
architected to determine the source of data needed, diagram the source data, and
determine the method for extraction and delivery.
4. Answer: D. Determine the monthly flat file transmission protocol. The goal of Source
Data Architecture is to ensure that the source data is extracted only once, define the scope
and implementation of the data warehouse and oversee the construction of the enterprise
data warehouse.
5. Answer: B. Data Distribution. Data Distribution is targeted towards the efficient
delivery of the proper information to the proper recipients. In Data Distribution data can
be streamed or supplied depending on the requirements of the communication.
6. Answer: C. Data Integration. Data Integration requires combining and matching
information in different sources, and resolving a variety of conflicts. XML is becoming a
de facto data integration standard.
7. Answer: A. Single point of authorization. A fundamental principle in Change
Authorization of Architectures is a single point of change authorization. Every change
must run the same process and authorization prior to changes are implemented.
8. Answer: A. Passive. In a client-server architecture, the features of the client are:
Active (Master), sending request, and waiting until reply arrives.
9. Answer: B. Active. In a client-server architecture, the features of the client are:
Passive (Slave), waiting for requests, and on requests serves them and send a reply.
10. Answer: B. 3-tier Architecture. If the networks consists of clients, application servers
which process data for the clients, and database servers which store data for the
application servers it is known as a 3-tier Architecture.
11. Answer: D: Company Directory. Metadata has many sources including but not
limited to: Tools, Applications and Software Packages.
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12. Answer: D. Metadata user. When beginning Metadata solution architecture, the
following type of analysis should be carried out: Process Flow, Metadata Flow, Metadata
Record Identification, and Metadata Categorization. Metadata occurs at the input and
output of processes the tools like case tools and applications gather the metadata. The
Metadata Record Identification identifies the data needed in a metadata solution and their
origins. The last step is to categorize the metadata for display and usage.
13. Answer: B. A Normalized schema. The Zachman Framework is a normalized schema
that is a good analytical tool. It is not a 36-cell matrix. The Zachman Framework logic is
neutral to methods and tools. Each cell of the Framework is unique and primitive.
14. Answer: D. Greater accounting staff effectiveness. Enterprise Architecture Planning
has the following benefits: consistency and compatibility of systems, interoperability
between systems and databases, economies of scale in purchasing and developing
systems, reduced overall system costs, and greater IT staff effectiveness.
15. Answer: C. Maintain a secure infrastructure and IT support for networks and
distributed systems. Enterprise Architecture Planning addresses: data management,
application environment and development toolsets, middle-ware and transaction
management, Web delivery environment, operating systems and other system software,
network environment, and hardware server and client environments.
16. Answer: A. Information Technology Strategy. An Enterprise Architecture Plan is
usually part of the overall Information Technology Strategy. The Information
Technology Strategy leads to Infrastructure Strategy, Information Technology
Architecture Plan and Application Infrastructure Plan.
17. Answer: D. Shred. The data life cycle phases are: Create/Store, Retrieve,
Modify/Update, Read/Use, Transport, Archive and Delete. The data lifecycle is the
process of managing data throughout its lifecycle from conception until disposal, within
the constraints of the data policy.
18. Answer: B. Data Life cycle management is an approach to managing an
organization's data. The definition of data life cycle management is an approach to
managing an organization's data that involves procedures and practices as well as
applications.
19. Answer: A. Information Engineering. Information Engineering is defined as "an
integrated and evolutionary set of tasks and techniques that enhance business
communication throughout an enterprise enabling it to develop people, procedures and
systems to achieve its vision".
20. Answer: D. Data Warehousing. Information Engineering has many purposes,
including organization planning, business re-engineering, application development,
information systems planning and systems re-engineering.
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21. Answer: A. Developing a migration strategy from one application environment to
another. Data reengineering is structured application redevelopment technique that
applies to the structure, function and meaning of data.
22. Answer: D. Prototyping. Prototyping is the process of quickly putting together a
working model in order to test various aspects of the design, illustrate ideas or features
and gather early user feedback.
23. Answer: A. Controls the organization, storage and retrieval of data in a database. A
Database Management System controls the organization, storage and retrieval of data in a
database. Two of the functions that a DBMS has are a modeling language to define the
schema and a database query language.
24. Answer: D. File. The common DBMS models are Hierarchical, Network and
Relational.
25. Answer: D. RAID Disk arrays. The functions of a DBMS are: A modeling language
to define the schema, a database query language and transaction method that ensures
Atomicity, Consistency, Isolation, and Durability (ACID). Many DBMS also support the
Open Database Connectivity API that supports a standard way for programmers to access
the DBMS. DBMS do not include a storage device.
26. Answer: A. Controls the organization, storage and retrieval of data in a database. A
Database Management System controls the organization, storage and retrieval of data in a
database. Two of the functions that a DBMS has are a modeling language to define the
schema and a database query language.
27. Answer: D. Structured Query Language. The functions of an ODBMS are: Object
Definition Language (ODL), Object Query Language (OQL), C++ and Java Binding.
Structured Query Language is a function of a Relational Database Management System.
28. Answer: C. Transforming metadata to fit business requirements. Metadata is created
when using ETL tools not manipulated by ETL tools. ETL tools involve the functions of
extracting data from data sources, transforming data to fit business requirements and
loading data into the data warehouse.
29. Answer: D. C++. The first part of an ETL process is to extract the data from the
common data source formats like Relational database, Flat Files, IMS or other data
structures such as VSAM or ISAM. Extraction extracts the data into a format for
transformation processing. C++ is a programming language not data.
30. Answer: D. DDL SQL statements with SQL variations. Typical functions of the
transformation process in ETL tools include: Translating code values (e.g. M for male or
F for Female), Deriving new calculated values (A+B = C), Joining or merging data from
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multiple sources, generating surrogate key values and summarizing values. The DDL
SQL statement with SQL Variations is a DB mechanism to create tables for example.
31. Answer: D. Insert audit trail records. Typical load functions of an ETL tool in a data
warehouse are to: Overwrite old information, insert new records and update old record
and insert a new record.
32. Answer: A. Specific to a DBMS. Data modeling tools are RDBMS-independent.
Data Modeling Tools: Produce a diagram summarizing the results of your data modeling
efforts; Generate a database schema from a model; and Diagram of referential integrity
constraints.
33. Answer: B. Database that tracks data element definitions. A data dictionary is a
database that tracks data element definitions. An encyclopedia is an internal database
that store information tracked, developed, and maintained by a predefined set of singlevendor tools. A data staging area is a set of cleansed, organized, and transaction level
data. Instances of characters could represent any time of file or data store and is not
specific to a data dictionary.
34. Answer: C. A repository or database of information. A data dictionary is a repository
or database of information. Data dictionaries can be used as a white page application and
network information service or as an LDAP directory service.
35. Answer: D. Internal database that store information tracked, developed, and
maintained by a predefined set of single-vendor tools. A data encyclopedia is an Internal
database that store information tracked, developed, and maintained by a predefined set of
single-vendor tools. A data dictionary is a database that tracks data element definitions.
A data staging area is a set of cleansed, organized, and transaction level data.
36. Answer: D. XML. XML is a format. A Data Registry is defined as an automated
resource “used to describe, document, protect, control and access informational
representations of an enterprise”. Typically the following is held in a data registry:
Standardized information in a pre-defined model, Metadata, system metadata, system
engineering; and Reference information. Standards for models and templates for data
and metadata registries already exist – for example, the ISO 11179 standard for Metadata
Registries, and ebXML for XML registries.
37. Answer: B. UDDI registry with a proprietary repository. A federated SOA
deployment requires a standards-based registry-repository the choices involve two
standards, UDDI and ebXML Registry. A UDDI registry offers a subset of capabilities
offered by an ebXML Registry. Published in a UDDI registry are pointers to service
artifacts such as WSDL. Published in an ebXML Registry is not just pointers to service
artifacts, but also the actual artifact. Thus, an ebXML registry-repository can be used for
governance of any type of service artifacts throughout their life cycles.
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38. Answer: C. Metadata registry. A metadata registry is defined as an automated
resource “used to describe, document, protect, control and access informational
representations of an enterprise”.
39. Answer: D. Metadata repositories. A Metadata repositories are “an integrated, virtual
holding area with vendor-independent input, access, and structure; used to directly store
metadata and/or metadata-based gateways to external metadata”.
40. Answer: A. Detect patterns in data that explain the present and predict the future.
The goal of business intelligence tools are to help end-users detect patterns in data that
explain the present and predict the future. End users use a number of techniques to
visually represent the data and slice and dice analysis while interactively exploring the
data online.
41. Answer: A. Pivot tables. New methods of interacting with data in extended OLAP
models are small multiples or multidimensional matrix of related graphs, Geospatial
analysis that combines cartographic elements and data information and Predictive
Analytics.
42. Answer: D. Spreadsheet. Data mining tools have issues in analyzing unstructured
data like e-mails, memos, marketing material, notes from internal groups, news, user
groups, chats, and whitepapers.
43. Answer: C. Classification and Taxonomy. A technique to increase searching of
unstructured data is classification and taxonomy. An Ontology Is “A specification of a
representational vocabulary for a shared domain of discourse -- definitions of classes,
relations, functions, and other objects -- is called ontology.” – T.R. Gruber
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3.0 Data Analysis and Modeling
Overview
Planning and requirements analysis are conducted as part of a project life cycle. The
scope of the project is typically defined during planning. The scope can be refined using
fact finding techniques like interviews or questionnaires. During requirements analysis,
the analyst discovers the client’s requirements and detailed information needed to build
the application and data model. The requirements are gathered using fact-finding
techniques like Surveys, questionnaires, JAD sessions or Legacy systems analysis.
The purpose of data modeling is to develop an accurate model, or graphical
representation, of the client's data requirements at different levels of abstraction. The data
model acts as a framework for the development of the new or enhanced application.
There are many data models that may be used in an organization. In a typical
organization, the order of creation of data models is Conceptual, Enterprise, Logical, and
Physical. There are also Dimensional Data Models that support on-line analytical processing
applications and Object Oriented Models that structures systems around the data. Regardless
of the data models used, they are useful for confirming requirements and leading to
development of the application.
Topics
Data / Metadata Analysis & Design
Fact Finding Techniques
Requirements Definition and Management
Data Model Components
Logical Data Model
Dimensional Warehouse
Object Oriented / UML
Data Representations in Process Models
Data / Metadata Model Management
Types of Data Models
Scope of Model and Metadata
Data Model Support
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Questions
3.1
Data / Metadata Analysis & Design
3.1.1 Fact Finding Techniques
1. What type of fact-finding technique works best when dealing with numerous
divisions?
A. Interviewing
B. Surveys, questionnaires
C. JAD session
D. Legacy systems analysis
2. What are the benefits of a JAD Workshop?
1. Communication and combined knowledge
2. Build consensus and ownership
3. Improve design quality
4. Design cross-functional solutions
A.
B.
C.
D.
1&2
2&3
3&4
All of the above
3. What type of fact-finding technique minimizes time and assists in narrowing scope?
A. Interviewing
B. Surveys, questionnaires
C. JAD session
D. Legacy systems analysis
4. What type of fact-finding technique is a systematic attempt to collect information
from a person?
A. Interviewing
B. Surveys, questionnaires
C. JAD session
D. Legacy systems analysis
5. What type of fact-finding technique is always used in data warehousing projects?
A. Interviewing
B. Surveys, questionnaires
C. JAD session
D. Legacy systems analysis
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6. What is the most appropriate type of question to ask in an interview?
A. Closed questions
B. Open-ended questions
C. Leading questions
D. None
7. When is an unstructured interview more appropriate than a structured interview?
A. When the interviewer wants to gain a broad based view on an issue that needs to
be explored.
B. Interviewer identifies gaps in the knowledge, which acts as a basis for questions .
C. When the interview needs to be goal-oriented.
D. It is never appropriate to be unstructured as you always need to be prepared.
3.1.2 Requirements Definition and Management
8. What is the next best step after gathering User Requirements for a new system?
A. Evaluation of current environment and documentation
B. Gap analysis / Future state creation
C. Business rules discovery, validation and documentation.
D. Data / process matrices creation.
9. What is the next best step after a current state environment evaluation?
A. Gap analysis
B. Future state creation
C. Business rules discovery, validation and documentation.
D. Data / process matrices creation.
10. What is the next best step after a current state environment evaluation and future
state creation?
A. Gap analysis
B. Business rules discovery, validation and documentation.
C. Data / process matrices creation.
D. Requirements tracking and management to implementation
11. Which statement does not describe a business rule?
A. It is a statement that defines some facet of the business.
B. It asserts business structure, or controls or influences performance of the business.
C. It is at the lowest level and cannot be decomposed further or it would lose
business meaning.
D. It specifies a Pre or Post condition of an entity.
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12. Which of the following statements is a business rule?
A. When a failure is reported, an expeditor is assigned by the maintenance
department who sends the failure form to the service desk for scheduling.
B. If Acct_num is between 0 and 5000 then the customer is a member of the branch
that may deposit money.
C. A customer places an order
D. A customer with preferred status should have its orders filled as soon as possible.
13. Which one is not part of a typical business rule creation process?
A. Discovery,
B. Validation
C. Documentation
D. Rule Engine
14. Which of the following is not an example language to express business rules?
A. UML
B. Z notation
C. BPEL
D. ABAP
15. What is the benefit of Requirements tracking and management to implementation?
A. To provide a matrix that has a listing of the requirements for the entire project.
B. To ensure the system performs as it should.
C. The information in the matrix includes the number assigned to the requirement, a
brief description, the date submitted to project, and the tracking of the
requirement as it relates to development.
D. Determines multi-modal requirements.
16. What is the best benefit of a Data / process matrix?
A. The matrix is used to represent relationships between data and process.
B. Shorten design lead times while maintaining product quality.
C. To clarify dependencies of information flow.
D. To document the meaningful relationships between both process and data as it
was related to the authors during the interviews.
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3.2
Data Model Components
3.2.1 Logical Data Modeling
17. What are the major components in an Entity-Relationship diagram?
A. Attributes, relationships, and associations;
B. Object types, relationships, associations, and supertype/subtypes;
C. Object types and transitions, associations, and supertype/subtypes;
D. States and transitions;
18. What does the following Entity-relationship diagram describe?
Negotiates
Price
Buyer
Agent
Seller
1. Real estate agent negotiates price between buyer and seller.
2. Buyer negotiates price with seller, through real estate agent
A.
B.
C.
D.
1.
2.
1&2
Neither 1 or 2
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19. In the following diagram, what are the subtypes?
Employee
Manager
1.
2.
3.
4.
A.
B.
C.
D.
Contract
Employee
Hourly
Employee
Employee
Manager
Contract Employee
Hourly Employee
1
2
3&4
2, 3 & 4
20. What is the definition of an Attribute?
A.
An atomic fact or characteristic, which describes an entity.
B.
A description of an entity occurrence.
C.
A decomposable fact or characteristic, which describes an entity.
D.
An association between entities.
21. Attributes roles do the following:
a. Identify an occurrence of an entity type (primary key)
b. Relate an occurrence of one entity type to an occurrence of another entity
type (foreign key)
c. Describe the entity or association
d. Derives values from other data values in the model.
A.
B.
C.
D.
1&2
1&3
1, 2, & 3
1, 2, 3, & 4
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22. What is the definition of cardinality?
A. Relate an occurrence of one entity type to an occurrence of another entity type;
B. The relative number of occurrences which may exist between a pair of entities;
C. A characteristic that describes something about an entity;
D. Identify an occurrence of an entity type;
23. What is a Mandatory relationship in Optionality?
A. At least one or many;
B. None, one, or many
C. One and only one
D. None or one
24. Describe the following data model:
Employee
works for
employs
A.
B.
C.
D.
Organization
Unit
Each Employee “works-for” one and only one Organization Unit
Each Employee “works-for” at least one or many Organization Unit
Each Employee “works-for” none, one or many Organization Unit
Each Employee “works-for” none or one Organization Unit
25. What is the relationship between the Primary Key and Foreign Keys?
A. One-to-one;
B. One-to-many;
C. Many-to-Many;
D. Foreign Keys do not relate entities;
26. What is the Attribute that uniquely identifies an entity is called?
A. Entity Type
B. Entity Occurrence
C. Primary Key
D. Foreign Key
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27. What is the difference between an Entity Type and Entity Occurrence?
A. An Entity Type is something that exists and is capable of being described and an
entity occurrence is a relationship;
B. An Entity Type is the definition and the entity occurrence is an instance of the
Entity;
C. An Entity Type is a physical object type and the entity occurrence is the project.
D. There is no difference.
28. What data model is the Normalization Process applied?
A. Conceptual Data Model
B. Logical Data Model
C. Physical Data Model
D. Metadata Data Model
29. What is the objective of the Normalization process?
A. To identify the one best place an attribute belongs.
B. To organize the physical design of the data model into tables and columns.
C. To organize columns based on the mathematical principles of set theory.
D. To assign an attribute to multiple entities.
30. What are the attributes of a data model in First Normal Form?
A. All repeating groups have been eliminated
B. Every attribute describes completely that entity and not an entity identified by
only part of the primary identifier.
C. Data items that do not describe the entire primary key of the entity are eliminated.
D. Identified restrictions that apply to the data and its relationships.
31. What are the attributes of a data model in Second Normal Form?
A. All repeating groups have been eliminated
B. Every attribute describes completely that entity and not an entity identified by
only part of the primary identifier.
C. Data items that do not describe the entire primary key of the entity are eliminated.
D. Identified restrictions that apply to the data and its relationships.
32. What are the attributes of a data model in Third Normal Form?
A. All repeating groups have been eliminated
B. Every attribute describes completely that entity and not an entity identified by
only part of the primary identifier.
C. Data items that do not describe the entire primary key of the entity are eliminated.
D. Identified restrictions that apply to the data and its relationships.
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33. What are the two principle types of static relationships in a class diagram?
A. Primary Key and Foreign Key.
B. Association and Subtype.
C. Cardinality and Optionality.
D. One-to-One and One-to-Many.
3.2.2 Dimensional Warehouse
34. When using Dimensional Modeling, a logical design technique, data is presented in
which manner?
A. Processes, data stores, flows and terminators.
B. Processes, data stores, relationships and flows.
C. Fact tables and dimension tables.
D. Entities, data, and relationships.
35. Which one of the following is best described as a fact:
A. promotion_name
B. clerk_grade
C. address
D. dollars_sold
36. In Dimensional Modeling, snowflaking is a technique that does the following:
A. Aggregates fact tables into summary tables for easier querying.
B. Adding more attributes into a single dimension table to make it easier for the
business user understandability.
C. Removes low-cardinality textual attributes from dimension tables and places them
in joined “secondary” dimension table.
D. Identifies cross-dimensional attributes for easier browsing performance.
37. Which application is the best fit for Dimensional Modeling?
A. OLTP
B. OLAP
C. HPC
D. Web
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38. In a Dimensional Model, when tracking changes in a slowly changing dimensional
table, the old value is discarded and has no significance is regarded as what type?
A. type 1
B. type 2
C. type 3
D. cross-dimensional attribute
39. In a Dimensional Model, when tracking changes in a slowly changing dimensional
table, the old value is recorded and has significance is regarded as what type?
A. type 1
B. type 2
C. type 3
D. cross-dimensional attribute
40. In a Dimensional Model, when tracking changes in a slowly changing dimensional
table, the old value and new value are equally important is regarded as what type?
A. type 1
B. type 2
C. type 3
D. cross-dimensional attribute
41. In the Customer Dimension, a Slowly Changing Type 2 Dimension, the key would
be described as the following?
A. Customer_Key which is a new generated, meaningless, key;
B. Customer_Key – made of Customer_Number appended with Time_Key;
C. Customer_Key – made of Customer_Number appended with version number;
D. Customer_Key from the old record because it will be overwritten.
E. Customer_Key – made of the Operational Key
3.2.3 Object Oriented / UML
42. What term in the basis of modularity and structure in object-oriented modeling?
A. Entity
B. Class
C. Object
D. Relationship.
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43. What is the definition of an object?
A. An instance of a class.
B. Behavior that varies depending on the class.
C. A type of privacy applied to the data and some of the methods.
D. A person, place or thing.
44. What is the term for a type of privacy applied to the data and some methods of a
class?
A. Encapsulation
B. Inheritance
C. Abstractions
D. Polymorphism
45. When modeling a class diagram, what is an association?
A. An association represents relationship between instances of classes.
B. A state of being associated.
C. Primary and Foreign Key relationship between objects.
D. There is no concept of an association in a class diagram.
46. Which one is not one of the relationship types in UML Modeling?
A. Generalization
B. Association
C. Aggregation, Composition
D. Polymorphism
47. What is the term for how many objects may participate in a given relationship in a
class diagram?
A. Cardinality
B. Optionality
C. Multiplicity
D. Pairing
48. In the following diagram, what does the association show in terms of multiplicity?
1
A.
B.
C.
D.
Exactly one.
Many.
Optional.
Number specified.
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49. In the following diagram, what does the association show in terms of multiplicity?
*
A.
B.
C.
D.
Exactly one.
Many.
Optional.
Number specified
50. In the following diagram, what does the association show in terms of multiplicity?
0..1
A.
B.
C.
D.
Exactly one.
Many.
Optional.
Number specified
51. In the following diagram, what does the association show in terms of multiplicity?
1..10
A.
B.
C.
D.
Exactly one.
Many (zero or more)
Optional (zero or one)
Number specified
52. What Association does the following diagram represent?
A.
B.
C.
D.
Aggregation.
Composition.
Ordered Role.
Not an association type.
53. What does the marking + on an attribute mean?
A. Public attribute
B. Protected attribute
C. Private attribute
D. Package attribute
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54. Which of the following is false in describing Attributes in UML?
A. Attributes are always single-valued.
B. Attributes may be optional or mandatory
C. Optional or mandatory may be drawn on the class diagram.
D. Attributes have name, type, and default values.
55. What is the term that defines the processes that a class can carry out?
A. Operations.
B. Specifications.
C. Multiplicity.
D. Notation.
3.2.4 Data Representations in Process Models
56. Which one of the following is not a true statement about triggers?
A. Triggers can accept parameters
B. Triggers are code that automatically execute on a table or data in response to
certain events.
C. Triggers can be used to enforce Referential Integrity between tables.
D. Triggers can be executed after SQL command type statements of: INSERT,
UPDATE, or DELETE.
57. Which one of the following is not a typical type of referential integrity trigger?
A. Identifying
B. Non-identifying
C. Subtype
D. Supertype
58. What is the difference between a stored procedure and a trigger?
A. Stored procedures can accept parameters while Triggers cannot.
B. Stored procedures are stored in the database while Triggers are not.
C. Stored procedures and procedural while Triggers can be used to enforce
Referential Integrity between tables.
D. Stored procedures and Triggers can simplify data management.
59. What is the benefit of the Unified Modeling Language (UML)?
A. UML standardizes representation of object oriented analysis and design.
B. In UML, requirements gathering comprise of Use Case and Activity diagrams.
C. In UML, design comprise Class and Object diagrams.
D. In UML, deployment comprises of Package and Subsystem diagrams.
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3.3
Data / Metadata Model Management
3.3.2 Types of Data Models
60. What data model is composed of subject areas, relationships, and subject area
definitions?
A. Conceptual Data Model
B. Logical Data Model
C. Physical Data Model
D. Dimensional Data Model
61. What is the difference between a Conceptual Data Model and Enterprise Data
Model?
A. A Conceptual Data Model is a type of Business Model while an Enterprise Data
Model is a physical data model.
B. A Conceptual Data Model describes the whole enterprise business subject areas
while an Enterprise Data Model is a decomposition of subject area entities.
C. A Conceptual Data Model is a logical data model while an Enterprise Data model
is a physical data model.
D. A Conceptual Data Model is a concept applied to the enterprise while an
Enterprise Data Model is applied to databases.
62. In a typical organization, the order of creation of data models is in which of the following
orders?
A. Conceptual, Enterprise, Logical, Physical
B. Enterprise, Conceptual, Logical, Physical
C. Logical, Conceptual, Enterprise, Physical
D. Physical, Logical, Enterprise, Conceptual
63. What data model is composed of tables and columns?
A. Conceptual Data Model
B. Logical Data Model
C. Physical Data Model
D. Dimensional Data Model
64. What data model is geared to a decision support environment?
A. Conceptual Data Model
B. Logical Data Model
C. Physical Data Model
D. Dimensional Data Model
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65. Which data model has an Enterprise Wide scope?
A. Conceptual Data Model
B. Logical Data Model
C. Physical Data Model
D. Dimensional Data Model
66. Which data model has all the entities for each subject area?
A. Logical Data Model
B. Physical Data Model
C. Dimensional Data Model
D. Enterprise Data Model
67. What are the elements in an Enterprise Data Model?
A. Use Cases and Activity diagrams
B. Entities and Relationships
C. Class and Object Diagrams
D. Tables and Relationships
68. What is the definition of a metamodel?
A. Data models that specify one or more other data models.
B. Models of unclassified metadata.
C. Physical metadata storage.
D. Models that describe the flow of metadata.
69. What is the difference between a metamodel and a meta-metamodel?
A. Metamodels are data models that specify other data models, while metametamodels defines ontology.
B. Metamodels are the definition of concepts while meta-metamodels defines the
language.
C. Metamodels describe the types of records, while meta-metamodels describe the
type of records used in the database.
D. There is no difference.
70. Which one of the following is not an industry standard?
A. Case Data Interexchange Format (CDIF)
B. Meta Data Coalition (MDC)
C. Common Warehouse Model (CWM)
D. Metadata Interchange Specification (MDIS)
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71. What is the benefit of universal / industry models?
A. Standard data models for an industry that may be used off the shelf.
B. Adapt to industry users' characteristics.
C. The data requirements have already been gathered and provided in use cases.
D. There is no benefit as customizations can take hundreds of hours.
72. Which one of the following phases is not part of the data life cycle?
A.
B.
C.
D.
Create/Store
Modify/Update
Delete
Shred
3.2.2 Scope of Model and Metadata
73. Which of the following is not true about the scope of an enterprise wide data model?
A. Supports a wide audience.
B. Provides a data picture of the business.
C. Causes internal gridlock and inconsistencies.
D. Capable of being easily extended to capture new requirements.
74. What is the scope of the enterprise wide data model?
A. Business Unit (i.e. Marketing)
B. Corporation
C. Geographic Unit (i.e. North America)
D. Functional Area (Information Technology)
75. Which statement is not true about the enterprise wide data model?
A. The corporate data architect owns the Enterprise wide data model.
B. The Enterprise wide data model is driven by the business.
C. Subject areas are areas of concern for the corporation.
D. The enterprise data model will frequently change.
76. A data model represents Marketing data for an organization. What is scope of data
model?
A. Enterprise
B. Business Area
C. Project Oriented
D. Subject Area
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77. A data model represents financial data for an organization. What is scope of data
model?
A. Enterprise
B. Business Area
C. Project Oriented
D. Subject Area
78. A project has begun to track costs for starting up operations in Plant A.
scope of data model?
A. Enterprise
B. Business Area
C. Project Oriented
D. Subject Area
What is
3.2.3 Data Model Support
79. When using a data modeling tool, which one of the following is not applicable when
creating data models?
A. Forward Engineering
B. Reverse Engineering
C. Create Logical and Physical data models
D. Split models of older versions into separate logical and physical
80. What is creating a data model from an existing database is known as?
A. Forward Engineering
B. Reverse Engineering
C. Create Logical and Physical data models
D. Importing prior version data model
81. Which one of the following is not a benefit of Reverse Engineering Functionality?
A. Maintain database
B. Change Database Type (e.g. from SQLServer to Oracle )
C. Analyze differences in databases
D. Create the Enterprise Data Model
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82. Which one is not a typical Data Model tool function?
A. Forward Engineering
B. Reverse Engineering
C. Comparisons
D. Improving
83. What is the benefit of the Comparisons in data modeling tools?
A. Keep data model and database synchronized.
B. Compare changes between data model and database.
C. Select objects that want to compare.
D. Selectively import or export changes.
84. The database administrator would like to create a database from an existing data
model, which data modeling tool function would they use?
A. Forward Engineering
B. Reverse Engineering
C. Comparisons
D. Version Control
85. When a data modeler would like to roll back a change to a data model, which
function they would use?
A. Change Control
B. Model Merge
C. Versioning
D. Submodeling
86. When a data modeler would like to create an enterprise model which data model
function would be used?
A. Change Control
B. Model Merge
C. Versioning
D. Submodeling
87. Which of the following is not a reason for using Model Merge feature?
A. To create an enterprise data model.
B. To bring individual data models together in a group.
C. Two previously unrelated projects have merged.
D. Comparing two data models to detect changes.
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88. Which one of the following does not apply when importing Customer data model
with New Customer data model?
A. Create a new data model
B. Merge New Customer into Customer data model
C. Merge Customer into New Customer data model
D. The functionality is not allowed.
89. Which one is not typically represented in the breadth of data models in data
modeling tools?
A. Enterprise Data Model
B. Logical Data Model
C. Physical Data Model
D. Business Process Model
90. What is the benefit of linkages and mappings between enterprise, logical, and
physical data models?
A. To define the different purposes of the data models in the application
development process.
B. To maintain links between the different data models.
C. Synchronize changes between data models.
D. Applying transformation functions to the data models.
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Quick Answers
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Detailed Answers
1. Answer: C. JAD Session: Joint Application Design session is a method for performing
analysis that brings specific parties together within a workshop environment. Surveys
and questionnaires must be made up of closed questions and typically involve
clarification and discussion after the results are tabulated. Interviews are best done in
small groups. Legacy system analysis involves examining and probing the legacy
systems.
2. Answer: D. All of the above are the benefits of a JAD Workshop.
3. Answer: A. Surveys, questionnaires. Surveys and questionnaire is an effective
technique to get opinions from a wide variety of stakeholders in an organization. Surveys
and questionnaires must be made up of closed questions and typically involve
clarification and discussion after the results are tabulated. Joint Application Design
session is a method for performing analysis that brings specific parties together within a
workshop environment to collect requirements. Interviews are best done in small groups
and can be used when scope is unknown. Legacy system analysis involves examining
and probing the legacy systems.
4. Answer: A. Interviewing. Surveys and questionnaire are typically done electronically.
Joint Application Design is a workshop environment to collect requirements. Interviews
are best done in small groups where heuristic questions can be asked. Legacy system
analysis involves examining and probing the legacy systems and may or may not involve
discussion with staff.
5. Answer: D. Legacy systems analysis. Legacy system analysis is used in data
warehousing projects to perform source(legacy system) to target(data warehouse)
transformations.
6. Answer: B. Open-ended questions. Open-ended questions cannot be answered with a
simple yes or no response and thus encourage the interviewee to provide more
information. Closed-ended questions give a yes or no answer. Leading questions put the
interviewee’s opinion into the question and do not give an opportunity for the interviewer
to answer without bias.
7. Answer: A. When the interviewer wants to gain a broad based view on an issue that
needs to be explored. The unstructured interview is used when the interviewer wants to
explore an issue and facilitates description of domain in a way that is easy for the
interviewee.
8. Answer: A. Evaluation of current environment and documentation. After gathering
user requirements for a new system, the next best step is an evaluation of the current
environment and documentation to complete the current state assessment. Next, a future
state can be derived with the linkages to the current state if needed. A gap analysis is
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then conducted to compare its current state with its future state to determine the variance
between business requirements and current capabilities.
9. Answer: B. Future state. After creating a current state environment evaluation, the
next best step is to complete the future state assessment. Next, a gap analysis is then
conducted to compare its current state with its future state to determine the variance
between business requirements and current capabilities.
10. Answer: A. Gap analysis. After creating a current state environment evaluation and
future state creation, the next best step is to complete the gap analysis. A gap analysis
compares current state with future state to determine the variance between business
requirements and current capabilities.
11. Answer: D. It specifies a Pre or Post condition of an entity. Business rules are put in
business terms not in terms of conditions on entities. A business rule:
• Is a statement that defines some facet of the business.
• Asserts business structure or controls or influences performance of the business
and can be applied across the organization.
• Is at the lowest level and cannot be decomposed further or it would lose business
meaning.
12. Answer: D. A customer with preferred status should have its orders filled as soon as
possible. Business rules should contain the words: must; must not; should; should not;
only; only if. The following did not qualify as business rules due to the following
reasons:
When a failure is reported, an expeditor is assigned by the maintenance department
who sends the failure form to the service desk for scheduling. Ordering of events in
the business rule is declarative. Business rules should be procedural.
If Acct_num is between 0 and 5000 then the customer is a member of the branch that
may deposit money. Business rules should not contain technology nomenclature but
be solely about the business.
A customer places an order. This rule can be decomposed into further rules.
13. Answer: D. Rule Engine. The typical business rule creation process is discovery,
validation and documentation. Business rules are discovered as part of a formal
requirement gathering process during the initial stages of design. Once they are
documented, they are validated to ensure consistency and non-conflicting business rules.
Finally, they are documented. In some organizations, software packages are used to store
business rules.
14. Answer: D. ABAP. ABAP is a language used in SAP. If the situation merits,
business rules can be expressed in a very formal language, for example: UML, Z
notation, Business Rules Markup Language, Business Process Execution Language
(BPEL) and Business Process Modeling Notation (BPMN).
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15. Answer: B. To ensure the system performs as it should. The benefit of Requirements
tracking and management to implementation is to ensure the system performs as it
should. The requirements should be classified in a matrix that has a listing of the
requirements for the entire project. The type of information about each requirement in
the matrix should consist of the following information: the number assigned to the
requirement, a brief description, the date submitted to project, and the tracking of the
requirement as it relates to development. The matrix can determine multi-modal
requirements.
16. Answer: C. To clarify dependencies of information flow. The matrix has the
following structure: the columns in the matrix represent the major processes, and the
rows the Entities and Attributes. Elements of the matrix thus represent interactions
between a Process and Data. Matrix elements marked with an X represent data/process
interactions.
17. Answer: B. Entity-relationship diagrams consist of two major components: Object
types and relationships. Object types represent a collection, or set, or objects that can be
identified uniquely, and can be described by one or more facts (represented by a
rectangular box). Relationships represent a set of connections, or associates, between the
object types (represented by diamonds). Associations represent a relationship that we
need to maintain information. The subtype/supertype indicator consists of an object type
and one or more subcategories; connected by a relationship.
18. Answer: C. The diagram shows that both descriptions are valid.
19. Answer: D. Manager, Contract Employee and Hourly Employee are examples of
subtypes. Employee is the general category and the subcategories are: Manager,
Contract Employee and hourly employee.
20. Answer: A. Attributes are an atomic fact or characteristic, which describes an entity.
It possible to differentiate between an entity and attribute by examining whether it can
stand-alone and hold meaning. For example Street Name only makes sense when it
resides in the context of Employee. Street Name is an attribute of Employee entity an as
such modifies Employee. Employee is an entity. One occurrence of Employee entity
might be the employee Johnson. Attributes such as Street Name, City, and Phone
Number are attributes, which describe or modify Employee Johnson.
21. Answer: D: 1, 2, 3, & 4 Attributes roles identify, describe and relate attributes.
22. Answer: B. Cardinality can be defined as the relative number of occurrences, which
may exist between a pair of entities. There are three kinds of relationships: one-to-one;
one-to-many; and many-to-many. In a one-to-one relationship between two entities, at
any one time there exists only one occurrence of the entity (“Customer” and
“Employee”). In a one-to-many relationship between two entities, at any one time, there
may exist multiple occurrences of the entity “Customer” for each entity of “Customer
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Type”. In a many-to-many relationship between two entities, multiple occurrences of the
entity “Employee” can exist for multiple occurrences of the entity “Skill”.
23. Answer: A. Mandatory means At least one or many and is denoted by a bar.
24. Answer: A. The Data Model describes the relationship that Each Employee “worksfor” one and only one Organization Unit. The model also states that Each Organization
Unit can have many employees working for it. Bar indicates a Mandatory relationship
between Employee and Organization Unit. The Circle indicates an Optional relationship
between Organization Unit and Employee.
25. Answer: B. One-to-many. The primary key of the “one” entity becomes the foreign
key of the “many” entity. Foreign key data columns are part of the “many” entity in a
one-to-many relationship that is identified through the Normalization Process.
26. Answer: C. Primary Key. The Attribute that uniquely identifies an entity is called a
Primary Key. Foreign key data columns are part of the “many” entity in a one-to-many
relationship that is identified through the Normalization Process. An Entity Type is the
definition and the entity occurrence is an instance of the Entity. An entity type is an
identifiable person, organization, place, event, concept or thing that exists and is capable
of being described like an Employee. An Entity Occurrence is a unique instance of the
entity type like one Employee may have the last name of Johnson, Smith, and Lee.
27. Answer: B. An Entity Type is the definition and the entity occurrence is an instance
of the Entity. An entity type is an identifiable person, organization, place, event, concept
or thing that exists and is capable of being described like an Employee. An Entity
Occurrence is a unique instance of the entity type like one Employee may have the last
name of Johnson, Smith, and Lee.
28. Answer: B. Logical Data Model. Normalizing the Logical Data Model to the
generally accepted 3rd normal form is required to make the model useful for translation
into a physical model that can be implemented with little redundancy and remove
potential data anomalies in the create, update and deletion of data. The Normalization
process is not applied to the conceptual, physical or metadata data models.
29. Answer: A. To identify the one best place an attribute belongs. The process of
normalizing data elements is a technique based on mathematical principles of set theory,
first introduced by Dr. E. F. Codd. Normalization is a systematic process of grouping
attributes into data entities. Normalization does not dictate any physical design like
tables or columns. Normalization finds the one best place that an attributes belongs not
multiple places.
30. Answer: A. All repeating groups have been eliminated. A data model in First Normal
Form has all repeating groups eliminated. A data model in Second Normal Form has
every attribute describes completely that entity and not an entity identified by only part of
the primary identifier. A data model in Third Normal Form has data items that do not
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describe the entire primary key of the entity are eliminated. A simple rhyme to remember
the ordering is "the key, the whole key and nothing but the key", so help me Codd.
31. Answer: B. Every attribute describes completely that entity and not an entity
identified by only part of the primary identifier. A data model in First Normal Form has
all repeating groups eliminated. A data model in Second Normal Form has every
attribute describes completely that entity and not an entity identified by only part of the
primary identifier. A data model in Third Normal Form has data items that do not
describe the entire primary key of the entity are eliminated. A simple rhyme to remember
the ordering is "the key, the whole key and nothing but the key", so help me Codd.
32. Answer: C. Data items that do not describe the entire primary key of the entity are
eliminated. A data model in First Normal Form has all repeating groups eliminated. A
data model in Second Normal Form has every attribute describes completely that entity
and not an entity identified by only part of the primary identifier. A data model in Third
Normal Form has data items that do not describe the entire primary key of the entity are
eliminated. A simple rhyme to remember the ordering is "the key, the whole key and
nothing but the key", so help me Codd.
33. Answer: B. Association and Subtype. The two principle types of static relationships
in a class diagram are association and subtype. Primary key and foreign key, cardinality
and optionality, and one-to-one and one-to-many describe data models.
34. Answer: C. Fact tables and dimension tables. Every dimensional model is composed
of one table with a multipart key called a fact table and a set of tables called dimension
tables that describe the dimensions of the fact table. Examples of dimension tables are:
Time, Store, Product, Customer, and Employee while the fact table could be: Sales. The
Data Warehouse Bus Architecture may be defined as: A master suite of conformed
dimensions and to standardize the definitions of facts. Process, data stores, flows and
terminators are part of data flow diagrams. Entities, data and relationships are part of
data modeling. Processes, data stores, relationships and flows are a combination of data
flow diagrams and data modeling.
35. Answer: D. dollars_sold is a fact attribute. The most useful facts in a fact table are
numeric, additive and continuously valued. Continuously valued means that every time
the attribute is sampled, it can take on different values. Dimension tables, most often
contain descriptive textual information. Clerk_grade, address and promotion_name
would all be dimensions.
36. Answer: C. Snowflaking removes low-cardinality textual attributes from dimension
tables and places them in joined “secondary” dimension table. Snowflaking may be used
both logically and physically. For example Customer may be separated from Customer
Type in two dimension tables.
37. Answer: B. OLAP. Dimensional Modeling is best used in OLAP type applications
for browsing, performance, and user understandability. OLTP systems or online
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transaction processing systems use a normalized data model. HPC or High Performance
Computing uses a normalized model that typically resides in memory for faster
transaction time. Web is similar to OLTP in the data model used.
38. Answer: A. type 1. In a Dimensional Model, when tracking changes in a slowly
changing dimensional table, the old value is discarded and has no significance is regarded
as type 1. Type 1 overwrites the old record and does not track changes. Type 2 tracks full
changes and partitions history of the dimension table. Type 3 tracks old and new
definitions on the same record. Cross-dimensional attribute is an attribute that describes
an attribute but may also be counted and could reside in either a fact or dimension table.
39. Answer: A. type 2. In a Dimensional Model, when tracking changes in a slowly
changing dimensional table, the old value is recorded and has significance is regarded as
type 2. Type 2 tracks full changes and partitions history of the dimension table. Type 1
overwrites the old record and does not track changes. Type 3 tracks old and new
definitions on the same record. Cross-dimensional attribute is an attribute that describes
an attribute but may also be counted and could reside in either a fact or dimension table.
40. Answer: A. type 3. In a Dimensional Model, when tracking changes in a slowly
changing dimensional table, the old value and new value are equally important is
regarded as type 3. Type 1 overwrites the old record and does not track changes. Type 2
tracks full changes and partitions history of the dimension table. Type 3 tracks old and
new definitions on the same record. Cross-dimensional attribute is an attribute that
describes an attribute but may also be counted and could reside in either a fact or
dimension table.
41. Answer: A. In the Customer Dimension, a Slowly Changing Type 2 Dimension, the
key would be Customer_Key, which is a new generated, meaningless, key or surrogate
key. The type 2 response requires the use of a surrogate key to fully track the changes of
the record. Type One Dimension is overwritten. Intelligence in the key like Time or
Version numbers should not be used when creating keys.
42. Answer: B. Class. The basis of modularity and structure in object-oriented modeling
is called class. A class is a grouping of data and behaviour for a concept. Entity and
Relationship are concepts from data modeling not object-oriented modeling. An object is
an instance of a class. Each object has its own data, though the code within a class.
43. Answer: A. An instance of a class. An object is an instance of a class. Each object
has its own data, though the code within a class.
44. Answer: A. Encapsulation. The term for a type of privacy applied to the data and
some methods of a call is known as encapsulation. Inheritance is a method of
generalization that creates subtypes. Abstraction is the ability of a function to have
different specifications. Polymorphism is the ability of objects belonging to different
types to respond to method calls of methods of the same name, each one according to the
right type-specific behavior.
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45. Answer: A. An association represents relationship between instances of classes.
When modeling a class diagram, an association represents the relationship between
instances of classes. Each relationship has two roles, both to and from each class.
46. Answer: D. Polymorphism. The relationship types in UML Modeling are
Generalization, Association and Aggregation, Composition. Generalization typifies an
inheritance relationship of subtype/supertype. Association represents the relationships
between objects. Aggregation and Composition are a special type of association that
specifies a HAS-A relationship. Polymorphism is the ability of objects belonging to
different types to respond to method calls of methods of the same name, each one
according to the right type-specific behavior.
47. Answer: C. Multiplicity. The term for how many objects that may participate in a
given relationship in a class diagram is multiplicity. Cardinality and Optionality are
terms in that describe participation in a relationship(optional, mandatory, etc.) and the
number of times the entities can participate (one-to-one).
48. Answer: A. Exactly one. The number one specifies exactly one.
49. Answer: B. Many (zero or more). The * defines many (zero or more).
50. Answer: C. Optional. The diagram refers to a many (zero or one) association.
51. Answer: D. Number specified. 1..10 specify the number of items in multiplicity.
52. Answer: A. Aggregation. The diagram represents an aggregation association. A
composition association would have a dark diamond. The ordered role is notated by a
star.
53. Answer: A. Public attribute. The marking + on an attribute means it is a public
attribute. The following is a listing for all the markings:
"+" for public
"#" for protected
"−" for private
"~" for package.
54. Answer: C. Optional or mandatory may be drawn on the class diagram. A diagram
cannot specify optional or mandatory attributes on a class diagram.
55. Answer: A. Operations. The term that defines what processes a class can carry out is
Operations. Operations that change the state of an object are called modifiers.
Operations and modifiers are typically used interchangeably.
56. Answer: A. Triggers can accept parameters. Triggers are code that automatically
execute on a table or data in response to certain events. Triggers can be used to enforce
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referential integrity between tables that have a relationship. There are typically three
triggering EVENTS that cause trigger to 'fire':
• INSERT event (as a new record is being inserted into the database).
• UPDATE event (as a record is being changed).
• DELETE event (as a record is being deleted).
57. Answer: D. Supertype. The following are typical types of referential integrity
triggers: Identifying, non-identifying (allowing nulls and non-null), and subtype. The
typical actions that the triggers can conduct are: CASCADE, RESTRICT, SET NULL
(non-identifying-nulls allowed), SET DEFAULT (non-identifying), and NONE.
58. Answer: A. Stored procedures can accept parameters while Triggers cannot. Stored
Procedure is a program which, like a Trigger is physically stored in a database. A stored
procedure and trigger can both be used to enforce Referential Integrity and simply data
management.
59. Answer: A. UML standardizes representation of object oriented analysis and design.
While all of the other statements are true, the overall benefit of UML is that it
standardizes representation of the object oriented analysis and design.
60. Answer: A. A conceptual model is composed of subject areas, relationships, and
subject area definitions. A Conceptual Model is a type of Business Model.
61. Answer: B. A Conceptual Data Model describes the whole enterprise business subject
areas while an Enterprise Data Model is a decomposition of subject area entities. A
Conceptual Model is a high-level starting-point for design and construction activities leading
to implemented information systems that fulfill important business needs. The Enterprise Data
Model is at a lower level of detail than the conceptual model. A typical conceptual model of a
whole enterprise might consist of 7-9 subject areas, representing major business subject areas.
An Enterprise Data Model (EDM) while still not containing all entities or all relationship will
have many entities for each subject area.
62. Answer: A. Conceptual, Enterprise, Logical, Physical. In a typical organization, the order
of creation of data models is Conceptual, Enterprise, Logical, Physical.
63. Answer: C. A Physical data model is composed of tables and columns. Physical data
models are representations of models that specify database or file structure implementations.
64. Answer: D. A Dimension Data Model is geared to decision support environments.
Data is modeled for retrieval of large amounts of data. Design for high volume retrieval is
coupled with specialized administration skills and techniques, and often specialized
dimensional database management systems.
65. Answer: A. Conceptual Data Model. A Conceptual Data Model describes the whole
enterprise business subject areas.
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66. Answer: D. Enterprise Data Model. An Enterprise Data Model (EDM) while still not
containing all entities or all relationship will have many entities for each subject area.
67. Answer: B. Entities and Relationships. An Enterprise Data Model is comprised of
Entities and Relationships that are organized into Subject Areas. The model may include
attributes of the Entities.
68. Answer: A. Data models that specify one or more other data models. Metamodels are
the details behind the metadata that depict metadata relationships.
69. Answer: A. Metamodels are data models that specify other data models, while metametamodels defines ontology.
70. Answer: B. Meta Data Coalition (MDC). Meta Data Coalition is a group that defined
the Metadata Interchange Specification. CDIF, CWM, MDIS are all industry standards.
71. Answer: A. Standard data models for an industry that may be used off the shelf.
Standard data models are widely used in an industry and shared among different
companies.
72. Answer: D. Shred. The data life cycle phases are: Create/Store, Retrieve,
Modify/Update, Read/Use, Transport, Archive and Delete. The data lifecycle is the
process of managing data throughout its lifecycle from conception until disposal, within
the constraints of the data policy.
73. Answer: C. Causes internal gridlock and inconsistencies. The Enterprise wide data
model known as a Conceptual Data Model describes the whole enterprise business
subject areas, supports the entire enterprise, a wide audience, provides a data picture of
the business that is capable of being easily extended to capture new requirements. The
Enterprise wide data model leads to and integrated data picture that breaks down
inconsistencies and promotes data knowledge, use and sharing.
74. Answer: B. Corporation. The Enterprise wide data model, known as the Conceptual
Data Model describes the whole corporation not just a business unit, geographic unit or
functional area.
75. Answer: D. The enterprise data model will frequently change as new requirements are
determined. The enterprise data model should remain stable unless for example, a new
company is acquired. The enterprise wide data model is driven by the business, which
encompasses areas of concern or importance to the corporation. The corporate data
architect owns the enterprise wide data model.
76. Answer: B. Business Area. The scope of a data model that represents Marketing data
for an organization is business area. The data model may be specific to Marketing area
needs or may have access restricted to the Marketing group. The data model would be a
part of the enterprise data model.
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77. Answer: D. Subject Area. The scope of a data model that represents financial data for
an organization is subject area. Every area in the organization needs financial
information of some sort, not just the Finance group. The Finance group will see the
financial data for an entire organization.
78. Answer: C. Project Oriented. The scope of a data model that tracks costs for starting
up operations in Plant A is Project Oriented. The data will only need to be collected
while the project is in progress, and analyzed after the closure of the project.
79. Answer: A. Forward Engineering. Forward Engineering the data model is already
created and the data modeling tool is creating the database scripts and will apply the
scripts to create a database and/or tables in the database. Reverse Engineering captures
information from a database or script file to create a physical data model. Creating
Logical and Physical data model involves using the GUI to create a data model that
conforms to business requirements. Splitting models of older version into separate logical
and physical data models can create more manageable data models.
80. Answer: B. Reverse Engineering. Reverse Engineering creates a data model from an
existing database. Forward Engineering the data model is already created and the data
modeling tool is creating the database scripts and will apply the scripts to create a
database and/or tables in the database. Creating Logical and Physical data model
involves using the GUI to create a data model that conforms to business requirements.
Importing a prior version data model is looking at an older version of the data model.
81. Answer: D. Create the Enterprise Data Model. Reverse Engineering benefits are:
maintaining database, changing database types, analyzing the differences in databases.
Reverse Engineering functionality cannot create the Enterprise Data Model unless the
database has enterprise wide data.
82. Answer: D. Improving. Forward Engineering, Reverse Engineering and Comparisons
are all typical Data Model tool functions. Improving is a function that does not exist in
data modeling tools. Forward Engineering the data model is already created and the data
modeling tool is creating the database scripts and will apply the scripts to create a
database and/or tables in the database. Forward Engineering is also referred to as
exporting. Reverse Engineering creates a data model from an existing database. The
comparison function allows for comparisons between the data model and database; select
the objects to compare and selectively import or export changes.
83. Answer: A. Keep data model and database synchronized. The benefit of Comparison
function in data modeling tools is that it allows the data modeler or database
administrator to keep the data model and database synchronized. The comparison
function allows for comparisons between the data model and database; select the objects
to compare and selectively import or export changes. These are features of the
comparison function. The main benefit is the data model and database is synchronized.
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84. Answer: A. Forward Engineering. Forward Engineering the data model is already
created and the data modeling tool is creating the database scripts and will apply the
scripts to create a database and/or tables in the database. Forward Engineering is also
referred to as exporting.
85. Answer: C. Versioning. Versioning records who made the change and when the
change was made to provide full project audit trail thus enabling rollback options when
comparing versions. Change control is a function that allows knowing the impact of
change before saving. Model Merge is simply merging two data models together.
Submodeling breaks a data model into smaller models (e.g. by subject area) for ease of
use.
86. Answer: B. Model Merge. Model Merge supports merging of data models in an
enterprise. Versioning records who made the change and when the change was made to
provide full project audit trail thus enabling rollback options when comparing versions.
Change control is a function that allows knowing the impact of change before saving.
Submodeling breaks a data model into smaller models (e.g. by subject area) for ease of
use.
87. Answer: D. Comparing two data models to detect changes. Model Merge features
can be used to bring two data models together. Model Merge can: create an enterprise
data model, bring individual data models together in a group, and merge two previously
unrelated projects. If a data modeler wanted to compare two data models to detect
changes, they would use a Comparison function if they were previously linked.
88. Answer: D. The functionality is not allowed. When importing and merging data
models, data modelers can typically: create a new data model or update one of the
existing data models.
89. Answer: D. Business Process Model. Although some tools offer linkages between
data model and business process tools and have synchronization options, the tools are still
typically separate. The benefit of this synchronization is it verifies the data models
support the business processes and vice versa. Data Modeling tools have the breadth of
data models that represent: Enterprise Data Model; Logical Data Model; and Physical
Data Model.
90. Answer: C. Synchronize changes between data models. The benefit of linkages and
mapping between enterprise, logical and physical data models is to synchronize changes
between data models. The data models do not need to be stored in the same file, but need
to maintain links between the different data models. Different data models are used as
they serve different purposes in the application development process. A transformation
function may include items like many-to-many where the relationship is dissolved by an
identifying entity.
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4.0 Data / Metadata Infrastructure Management
Overview
Establishing data modeling standards provides a framework and guidance for new
projects being implemented and provides a strong foundation for changes applications
and the data. Standards, like entity naming, increase data sharing opportunities, reduce
data redundancy, and improve application interoperability.
Data security and privacy is an area of growing legal regulation and enforcement. Data
security can be derived from the three principles of: Accountability, Authorization, and
Availability. Accountability is the concept that every user must be responsible for their
actions, so that in the event of any questionable activity or breach of policy, a specific
user can be identified. Authorization is a concept that access to data and system resources
should be limited to a need to know basis, and that specific users must be specifically
allowed such access. Availability is the concept that system and data resources must be
accessible whenever they are needed. Data privacy is being classified by the content,
critically to business and availability requirements. OECD Guidelines define the
Protection of Privacy and Transborder Flow of Data and provide privacy protection in
relation to personal data.
Topics
Standards, Policies, Procedures, Guidelines
Standards Management Process
Data Models
Data Elements
Data Security and Privacy
Data Security Principles
Data Security Policy Types
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Questions
4.1 Standards, Policies, Procedures, Guidelines
1. What is the name of an entity?
A. Customer
B. Customers
C. Customer_Name
D. Name
2. Which is the best name for an entity describing employee information:
A. EmployeeTable
B. Employee_Table
C. EmployeeTbl
D. Employee
3. Which attributes follows best practises in naming?
A. Social-insurance-number
B. Social-insurance
C. Social-insurance-code
D. Social-insurance-numbers
4. When naming primary keys attributes follows best practises in naming?
A. Customer-Ident
B. Customer-Ids
C. Customer-Id
D. Customer-Identifier
5. Which of the following should be used when naming a relationship between an
employee and a manager?
A. Supervises
B. Supervise
C. Supervisor
D. Is a
6. Which of the relationships capture a hierarchal relationship between employees:
A. Has a
B. Supervises
C. Is a
D. Manages
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7. Which of the following is the best description of a business rule:
A. Repeat customers are categorized as type 01.
B. Repeat customers are good for business.
C. Repeat customers do not need a credit check.
D. Repeat customers do not need a credit check if they are in good standing.
8. Data Integrity rules in a data warehouse environment include the following except:
A.
B.
C.
D.
Cleaning
Redundancy resolution
Business rule enforcement
Random sampling.
9. What is the difference between ANSI and ISO/IEC?
A. ISO/IEC form the specialized system for worldwide standardization, while ANSI
that administers and coordinates the U.S. voluntary standardization and
conformity assessment system.
B. ISO/IEC cover the field of information technology while ANSI is concerned with
certification.
C. ISO/IEC cover the field of information technology while ANSI is concerned with
Product Standards
10. After the creation of a Standards Management Process, which of the following is not
considered:
A. Approval
B. Censure
C. Enforcement
D. Maintenance
11. Which of the following is not a notations typically used in logical and physical
database design:
A. Information Engineering
B. IDEF1x
C. Dimensional Modeling
D. Conceptual Modeling
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12. Which one of the following is not a data element representation types:
A. Amount
B. Code
C. Date
D. Customer
13. A data element name that conforms to the ISO/IEC 11179 metadata registry naming
convention does not have one of the following:
A. Object
B. Property
C. Representation term
D. Process Definition
14. In the ISO/IEC 11179 metadata is defined as:
A. Data about data
B. Data that defines and describes other data
C. DNA of the data
D. All information that is not the data itself.
15. What is the process for creating metadata when using a metadata registry?
A. Attributing, Classifying, Defining, and Registering
B. Attributing, Classifying, Defining, and Maintaining
C. Creation, Approval, Enforcement, Registering
D. Creation, Approval, Enforcement, Maintenance
16. Which of the following is not considered a sampling technique of data element audits:
A. Random Sampling
B. Systematic Sampling
C. Cluster Sampling
D. Standard Deviation.
17. In a data element audit, the data element must be:
A. Valid and accurate
B. Conformance of data values to its domain and business rules
C. Complete
D. Not null
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18. In a Data Warehouse, legacy element linkages are referred to as:
A. Source System Metadata
B. Data Staging Metadata
C. DBMS Metadata
D. Front Room Metadata
19. What should be standard for knowledge workers that analyze data in a data
warehouse?
A. Use data warehouse record and metadata to navigate back to the record in the
source system.
B. Complete the data in a data warehouse by adding postal information.
C. Ability to consolidate the data in the data warehouse.
D. Conduct a baseline assessment of the quality of the data.
20. Which of the following definition uses the best example of metadata principles?
A. Employee ID Number - Number assigned to an employee. Employee - Person
corresponding to the employee ID number.
B. Employee – For the purpose of the data dictionary, employee is an internal
stakeholder.
C. Employee - an individual who has entered into or works under (or, where the
employment has ceased, worked under) a contract of employment.
D. Employee - Someone who is hired by HR under the DMSP to provide services to a
company.
4.2 Data Security and Privacy
21. The need for data security can be derived from three principles, which of the
following does not apply?
A. Agreement
B. Accountability
C. Authorization
D. Availability
22. Logging into a system by supplying a user name and password is known as which
data security principle?
A. Accountability
B. Authorization
C. Availability
D. Not a valid data security principle
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23. Authorization may be defined as:
A. Authorization is a concept that access to data and system resources should be
limited to a need to know basis, and that specific users must be specifically
allowed such access.
B. Authorization is the ability to grant power to make decisions from the data.
C. Authorization is the policy that gives official instructions on the use of the data.
D. Authorization is a digital document that describes a written permission from the
issuer to use a service or a resource that the issuer controls or has access to use.
The permission can be delegated.
24. Which data security principle defines that the system and data resources must be
accessible whenever they are needed?
A. Accountability
B. Authorization
C. Availability
D. Not a valid data security principle
25. What is the main objective of the data steward responsibilities with respect to
defining security policy?
A. Define the security requirements, controls and mechanisms applicable to all data
assets.
B. Management of the data asset accessibility.
C. Ensure compliance to the security policy.
D. Define the data quality standards.
26. When defining the security policy, what is the trustee responsibility?
A. Define the security requirements, controls and mechanisms applicable to all data
assets.
B. Management of the data asset accessibility.
C. Ensure compliance to the security policy.
D. Define the data quality standards.
27. What does the OECD Guidelines on the Protection of Privacy and Transborder Flows
of Data protect?
A. Provides privacy protection in relation to personal data.
B. Determines how the data is transmitted between countries.
C. Determines which countries can share data.
D. Outlines the handling of data.
28. Which one of the following is not a typical Government security classified level?
A. Top Secret / Secret
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B. Confidential
C. Restricted
D. Classified
29. Which of the following applies the most when classifying data in a data warehouse?
A. Only the summarized financial information needs to be confidential.
B. Only detailed financial records need to be confidential.
C. Data classification depends on the data content.
D. All data in a data warehouse should be available.
30. Audit trail inspection is a classified under what type of security monitoring?
A. Proactive
B. Reactive
C. Offensive
D. Defensive
31. When data is considered mission critical, what is the class and data availability
required, according to the Storage Networking Industry Association (SNIA) based upon
Five 9s?
A. Class 2 - 99% data availability
B. Class 3 - 99.9% data availability
C. Class 4 - 99.99% data availability
D. Class 5 - 99.999% data availability
32. When data is classified as Class 1 90% data availability, what is the business
classification of the data according to the Storage Networking Industry Association?
A. Not important to Operations
B. Important for Productivity
C. Business Important Information
D. Business Vital Information
33. Which of the following statements is the not true when data content is controlled in
an organization?
A. Business policies or regulatory rules require some/all data be retained “X” period
of time.
B. May need to prove records stored are “trustworthy” at later date
C. High data value to an organization need to be accessible, available and protected
D. Data availability is Class 1 – 90% data availability.
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34. Which of the following statements is the not true when data content is non-controlled
in an organization?
A. No business rules or regulations are requiring this data be kept for “X” period of
time.
B. Business just needs to keep the data archived and accessible
C. High data value to an organization needs to be accessible, available and protected.
D. Med-Low data value to an organization needs to be accessible and available.
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Quick Answers
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Detailed Answers
1. Answer: A. Customer. Naming an entity should follow a standard and uniform
approach. Entity Names should be simple, clear, and expressed in business terms.
Entities should be in noun or adjective noun format; singular; in business terms; and not
process specific. Customers are plural. Customer_Name is not in business terms. Name
is not clear to which name it is referring.
2. Answer: D. Employee. The best name for an entity describing employee information
is simply Employee. Incorrect names include: EmployeeTable, Employee_Table, and
EmployeeTbl. Employee_Table is not in business terms. EmployeeTbl is not clear.
EmployeeTable is not in business terms.
3. Answer: A. Social-insurance-number. An attribute should be in the singular,
consistent, and clearly defined in business terms. Properly defined attributes should
define the data domain type like date, time, amount, code, name, quantity and
description. Social-insure is unclear domain type. Social-insurance-numbers is plural.
Social-insurance-code does not define the contents.
4. Answer: D. Customer-Identifier. An attribute should be in the singular, consistent, and
clearly defined in business terms. Completely spelling out the attribute is preferred. If
using an abbreviation, they should be as clear as possible and used consistently across the
enterprise. If using abbreviations, whenever possible, use industry standard
abbreviations.
5. Answer: A. Supervises. Relationships capture how two or more entities are related to
one another. Relationships should be named after plural verbs.
6. Answer: C. Is a. Is a captures a hierarchal relationship between employees and the
different classes of employees or subtypes. In this relationship, the new class or object
has data or behavior aspects that are not part of the inherited class.
7. Answer: C. Repeat customers do not need a credit check. Business rules describe the
operations, definitions and constraints for governing policies. The business rule needs to
define in clear and concise business terms and easily applied.
8. Answer: D. Random sampling. Data integrity rules in a data warehouse environment
include: cleaning, redundancy resolution and business rule enforcement. Random
sampling is a technique and tool that may be used to conduct checks on the data.
9. Answer: A. ISO/IEC forms the specialized system for worldwide standardization,
while ANSI that administers and coordinates the U.S. voluntary
standardization and conformity assessment system. ISO (the International
Organization for Standardization) and IEC (the International Electro technical
Commission) form worldwide standards through technical committees. ANSI (The
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American National Standards Institute), is the US representative to the
ISO/IEC.
10. Answer: B. Censure. After the creation of a Standards Management Process, censure
is not a considered activity. The four activities associated with the Standards
Management Process are: Creation, Approval, Enforcement, and Maintenance.
11. Answer: D. Conceptual Modeling. The notations typically used in logical and
physical database design are: information engineering, IDEF1x (ICAM DEFinition
Language) and dimensional modeling. Conceptual Modeling is a high-level data model
that leads to a logical model.
12. Answer: D. Customer. The following is not a data element type: Customer. Data
element representation types are items like: Amount, Code, Date, Identifier, Name,
Number, Text, Rate, and Year. Data elements are the fundamental units of data an
Enterprise manages.
13. Answer: D. Process Definition. A data element name that conforms to the ISO/IEC
11179 metadata registry naming convention does not have of the following: Process
Definition. It does have an object, property and representation term (Date, Time, etc)
14. Answer: B. Data that defines and describes other data. In the ISO/IEC 11179
metadata is defined as data that defines and describes other data. It specifies the kind and
quality of metadata necessary to describe data, and it specifies the management and
administration of that metadata in a metadata registry.
15. Answer: A. Attributing, Classifying, Defining, and Registering. The processes for
creating metadata when using a metadata registry are: Attributing, Classifying, Defining,
and Registering.
16. Answer D. Standard Deviation. The following is not considered a sampling
technique of data element audits: Standard Deviation. Random, Systematic, and Cluster
are all sampling techniques. Random chooses any record in the database. Systematic
chooses every nth record. Cluster takes a subgroup of data based on a classification
method like city. Standard deviation would be used to model the sample of data and
calculate confidence that the data represents the whole.
17. Answer: A. Valid and accurate. In a data element audit, the data element must be
valid and accurate. A data element could be complete (not null) and conform to its data
values in its domain and business rules but still be inaccurate.
18. Answer: A. Source System Metadata. In a Data Warehouse, legacy element linkages
are referred to as: Source System Metadata. DBMS Metadata is descriptions about the
logical or physical data model. Data Staging Metadata is metadata around the staging
area. Front Room Metadata is metadata for the front line users.
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19. Answer: A. Use data warehouse record and metadata to navigate back to the record
in the source system. Prior to the source data being transformed to the data warehouse, it
should be standardized, cleansed, completed, enhanced, consolidated and summarized
where needed. The knowledge workers should be able to analyze the degree to which the
data agrees with original source. Complete the data in a data warehouse by adding postal
information and the ability to consolidate the data should be done prior to loading the
data into the data warehouse. The knowledge worker should be told of the data quality in
the data warehouse. In analyzing the data, they should not have to conduct a baseline
assessment of the quality of the data.
20. Answer: C. Employee - an individual who has entered into or works under (or, where
the employment has ceased, worked under) a contract of employment. Metadata
principles are: state the essential meaning of the concept; be precise and unambiguous
(Answer B); contain only commonly understood abbreviations(Answer C); be concise; be
able to stand alone; be expressed without embedding rationale, functional usage, domain
information, or procedural information; avoid circular reasoning (Answer A); and use the
same terminology and consistent logical structure for related definitions.
21. Answer: A. Agreement. The need for data security can be derived from the three
principles of: Accountability, Authorization, and Availability.
22. Answer: A. Accountability. Accountability is the concept that every user must be
responsible for their actions, so that in the event of any questionable activity or breach of
policy, a specific user can be identified. The specific security services that support
accountability are identification, authentication, and auditing. Identification refers to a
security service that recognizes a claim of identity by comparing a userid offered with
stored security information. Authentication refers to a security service that verifies the
claimed identity of the user, for example a password. Auditability refers to a security
service that records information of potential security significance. Authorization is a
concept that access to data and system resources should be limited to a need to know
basis, and that specific users must be specifically allowed such access. Availability is the
concept that system and data resources must be accessible whenever they are needed.
23. Answer: A. Authorization is a concept that access to data and system resources
should be limited to a need to know basis, and that specific users must be specifically
allowed such access. Access control refers to a security service that allows or denies a
user request based on privilege, group information, or context. The specific security
services that support authorization are access control and confidentiality. Confidentiality
refers to a security service that prevents disclosure of information to unauthorized parties
while the information is in use or in transit, or while the information is being stored or
destroyed. Accountability is the concept that every user must be responsible for their
actions, so that in the event of any questionable activity or breach of policy, a specific
user can be identified. Availability is the concept that system and data resources must be
accessible whenever they are needed.
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24. Answer: C. Availability. Availability is the concept that system and data resources
must be accessible whenever they are needed. The necessity for availability is dependent
upon your particular business proposition. The specific security service that supports
availability is integrity. Integrity refers to a security service that guarantees data has not
been altered, deleted, repeated, or rearranged during transmission, storage, processing, or
recovery. Accountability is the concept that every user must be responsible for their
actions, so that in the event of any questionable activity or breach of policy, a specific
user can be identified. Authorization is a concept that access to data and system
resources should be limited to a need to know basis, and that specific users must be
specifically allowed such access.
25. Answer: B. Management of the data asset accessibility. The main objective of the
data steward responsibilities with respect to defining the security policy is the
management of the data asset accessibility. The data steward does not ensure compliance
to the security policy, define the data quality standards, or define the security
requirements, controls and mechanisms applicable to all data assets.
26. Answer: A. Define the security requirements, controls and mechanisms applicable to
all data assets. The trustee is entrusted with the administration of the data assets.
27. Answer: A. Provides privacy protection in relation to personal data. OECD
Guidelines on the Protection of Privacy and Transborder Flow of Data provides privacy
protection in relation to personal data. The Guidelines apply to personal data, no matter
if the company is public or private sectors, because of the potential detriment to civil
liberties. The guidelines specify how to collect, store, process or disseminate personal
information.
28. Answer: D. Classified. The typical security classified levels are top secret, secret,
confidential, restricted and the lowest level of unclassified. The levels determine the
impact on national security if the data was to be made public. Corporations typically
have a similar type of classification structure.
29. Answer: C. Data classification depends on the data content. When classifying data in
a data warehouse, the data content needs to be evaluated. In some cases the detailed data
should be confidential and in others, the summarized data. The challenge in data privacy
is to share data while protecting the identifiable information.
30. Answer: D. Defensive. Audit trail inspection is classified under a Defensive type of
security monitoring. Other types of defensive monitoring are: Role Based Access
Security and Process Rights Management. Offensive monitoring includes: Provisioning
and Federated Identity Management.
31. Answer: D. Class 5 – 99.999% data availability. Mission critical data availability
according to the Storage Networking Industry Association based upon Five 9s is Class 5 99.999%. The five classes are:
Class 1 - 90% data availability: Not important to Operations
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Class 2 - 99% data availability: Important for Productivity
Class 3 - 99.9% data availability: Business Important Information
Class 4 - 99.99% data availability: Business Vital Information
Class 5 - 99.999% data availability: Mission Critical Information
32. Answer: A. Not important to Operations. When data is classified as Class 1 – 90%
data availability, the business classification of the data according to the Storage
Networking Industry Association is Not Important to Operations. The five classes are:
Class 1 - 90% data availability: Not important to Operations
Class 2 - 99% data availability: Important for Productivity
Class 3 - 99.9% data availability: Business Important Information
Class 4 - 99.99% data availability: Business Vital Information
Class 5 - 99.999% data availability: Mission Critical Information
33. Answer: D. Data availability is Class 1 – 90% data availability. When data content is
controlled in an organization, the organization has a compliance or fiduciary
responsibility for the data. Therefore, the controlled data has a high data value to an
organization and needs to be accessible, available and protected in a “trustworthy” state
and retained for a defined period of time. Data Value defines the business significance of
different classes of data and the degree to which data has to be accessible, available, and
protected.
34. Answer: C. High data value to an organization needs to be accessible, available and
protected. When data content is non-controlled in an organization, the organization does
not have a compliance or fiduciary responsibility for the data, only a business usage.
Therefore, the controlled data has med-low data value to an organization and needs to be
archived and accessible and the retention period is defined by the business and useful of
the data, not an external entity.
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5.0 Information Quality Management
Overview
Information quality is as important as the data in an organization, but often takes a back
seat. If the data cannot be depended upon, used and leveraged within the organization it
holds no value, ergo “Garbage In, Garbage Out”. Information Quality Principles set the
foundation for evaluating the quality of data through: Information Quality
Characteristics, Data Definition (or Information Product Specification) and Quality
Characteristics.
Once the “parameters” of the data have been define, information quality assessment and
ongoing information audits should occur. Audit and assessments measure the quality of
data, either in physical form (file, database, spreadsheet) or output from a process.
Accessing the data may take the form of Random sampling, Cluster sampling or
Systematic sampling. All results would be written in an Information Quality Report for
either further investigation or action.
Once the Information Quality improvements have been identified, and their root cause
determined, the process that produces the defective data must be fixed, plus the physical
data. A cycle of continuous improvement of the data quality needs to be implemented in
every organization. One approach, the Shewhart cycle, named for Walter Shewhart,
discussed the concept in his 1939 book, "Statistical Method From the Viewpoint of
Quality Control", is the continuous improvement cycle of Plan, Do, Check, Act. Overall,
organizations who want to maximize data and data management by exposing, mitigating
and managing quality within their business will reap the rewards to prevent first and
restore second.
Topics
Information Quality Principles
Definition
Information Quality Characteristics
Data Definition (or Information Product Specification) Quality Characteristics
Information Quality Assessment / Audit
Quality Assessment Characteristics
Quality /Cost Measurement
Information Quality Improvement
Data Corrective Maintenance
Data Movement Control
Information Quality Process Improvement
Information Quality Culture Transformation
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Questions
5.1 Information Quality Principles
1. What is the difference between data and information?
A. Data is the representation of facts, information is data in context.
B. Data is the context and Information is the content.
C. Data is the value and Information is a valuable enterprise resource.
D. Data is meaningful and information has context.
2. What is Information Quality?
A. Quality data that enables knowledge workers to answer their questions.
B. Correctness or accuracy of the data and the degree of usefulness and value data
has to the organization.
C. Quality data values in a data attribute field.
D. Valid and meaningful information that the enterprise can make decisions upon.
3. Who benefits the most from Information Quality?
A. Knowledge Workers
B. Management
C. Data Modeler
D. Data Base Administrator
4. What type of data is required when the consequences of nonquality cause major
process failure?
A. Complete Quality data
B. Accurate data
C. Scientific data
D. Zero-defect data
5. According to Larry English, what are the components that make up information?
A. Information = Data + Definition
B. Information = Data + Definition + Quality
C. Information = Data + Definition + Presentation
D. Information = Data + Definition + Content
6. What is Data Definition Quality?
A. The definition, quality, and accuracy that govern data.
B. The definition, domain value set, and business rules that govern data.
C. The information about the data that meets manager expectations.
D. The information that departmental knowledge workers depend on.
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7. What is needed to produce consistent high-quality information?
A. Information Product Specification
B. Data Definitions
C. Customer Requirements
D. Data Models
8. What are the features of Data Definition Quality?
A. Data standards quality, data definition quality and information architecture
quality.
B. Data standards quality, data model quality and information architecture quality.
C. Data models quality, domain type consistency, and definition quality.
D. Business rule quality, data definition quality, and domain value quality.
9. What is Data Name quality?
A. The name of the data plainly conveys the meaning of the objects named.
B. The name of the data enables knowledge workers to easily define data completely
and consistently across the organization.
C. The name of the data that the enterprise needs to know about is consistent with
the entity name.
D. The name of the data indicates the type of data represented.
10. What is “Definition Conformance”?
A. The measure of conformance of data its domain.
B. The measure of conformance of data to the information product specification.
C. To be consistent with the right level of granularity of the data values.
D. To be consistent with the meaning of actual data values with its data definition.
11. Which of the following describes “the right level of granularity in the data values”?
A. Definition Conformance
B. Completeness
C. Accuracy
D. Precision
12. Which of the following describes “the characteristic of having all required values for
the data fields”?
A. Definition Conformance
B. Completeness
C. Accuracy
D. Precision
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13. Which of the following depends on a reliable and precise recording of information?
A. Definition Conformance
B. Completeness
C. Accuracy
D. Precision
14. Which one of the following does nonduplication principles lead to the least?
A. Identical values in multiple records.
B. Duplicate records of a single event.
C. Same data maintained in many independent, distributed databases.
D. Its not a problem if there are standards and controls.
15. Which of the following describe when data is semantically equivalent?
A. Consistency of Redundant data
B. Timeliness
C. Usefulness
D. Objectivity
16. What does Information Float describe?
A. The timetable to gather data.
B. Length of time from when data is known, until it is available for a specific process
or use.
C. The average time required for data to be disseminated in the organization.
D. The degree of variance in the data.
17. In a decision support system, which of the following is considered most useful?
A. Tabular data
B. Data list
C. Graphic presentation (bar chart, etc)
D. It depends on the type of data
18. What is presentation clarity?
A. The degree the knowledge worker can understand the meaning of the data through
the presentation.
B. Statements of fact with a neutral point of view including reporting without bias,
and emphasis on initutive presentation to the knowledge worker.
C. The ability to see the legends, rows and columns in a bar chart.
D. The redesign producing high quality data content or presentation.
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19. Which one is a statement of fact with a neutral point of view, reporting without bias,
and emphasis on initutive presentation to the knowledge worker called?
A. Consistency of Redundant data
B. Timeliness
C. Usefulness
D. Objectivity
20. What are the primary inherent information quality characteristics?
A. Information Product Specification, Data Definitions, Data Models.
B. Definition, domain value set, and business rules that govern data.
C. Definition conformance, completeness, validity, accuracy, precision,
nonduplication, and consistency of redundant data.
D. Accessibility, timeliness, contextual clarity, derivation integrity, usability,
completeness.
21. What are the primary pragmatic information quality characteristics?
A. Information Product Specification, Data Definitions, Data Models.
B. Definition, domain value set, and business rules that govern data.
C. Definition conformance, completeness, validity, accuracy, precision,
nonduplication, and consistency of redundant data.
D. Accessibility, timeliness, contextual clarity, derivation integrity, usability,
completeness.
22. Which definition for Employee-start-date conforms best to the Attribute definition
quality principles?
A. The date an employee was hired.
B. The date a new employee was hired.
C. The date that an employee first started with the company regardless of location.
D. Tells when the employee first came to work
23. Using data name and definition consistency principles, which attributes best
describes: The date a service is started with the customer?
A. Product-start-date
B. Service-start-date
C. Service-release-date
D. Data-Service-release-date
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24. Which of the following is the most appropriate to demonstrate Entity Type Name
Clarity?
A. Retail Customer
B. Customers
C. Buying Customers
D. RTL_CUST
25. Which of the following is the most appropriate to demonstrate Attribute Name
Clarity?
A. Retail Customer Name
B. Customers Identities
C. Buying Customers Names
D. RTL_CUST_NAME
26. Which one of the following is not known as a domain type?
A. Date
B. Time
C. Amount
D. Customer
27. For a cross-reference attribute, what is the most appropriate abbreviation following
acronym clarity principles?
A. Xref
B. Cross-ref
C. X-reference
D. Crss-rfrnc
28. Why should names be appropriate to the knowledge workers?
A. Attributes names are consistent where facts context are equivalent.
B. Attributes names are equivalent to ease dissemination of information.
C. Attribute names are accurate to express the meaning of the fact being defined.
D. Attribute names are clear and concise business terms.
29. What role in an organization is responsible for the enterprise wide glossary?
A. Business Steward
B. Knowledge Worker
C. Data Modeler
D. Database Administrator
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30. What role in an organization is responsible for the business term definition?
A. Knowledge Worker
B. Data Modeler
C. Database Administrator
D. Subject Matter Expert
31. Where should business terms be defined?
A. Glossary
B. Data Model
C. Data Dictionary
D. Database
32. Which of the following is not true for Business rules:
A. Based on technical or existing system limitations.
B. Expressed in business terms.
C. Complete and specific.
D. Defines an aspect of business to take action.
5.2 Information Quality Assessment / Audit
33. Why is an information quality assessment/audit conducted?
A. To measure the quality of data and information of processes.
B. To measure the quality of data, either in physical form(file, database, spreadsheet)
or output from a process.
C. To measure the quality of data and information for statistical quality control.
D. To assess the stable processes.
34. Which of the following is not a purpose of information quality assessment?
A. Check the inputs and outputs of processes to ensure accuracy.
B. Declare the accuracy and reliability of data.
C. Provide feedback to the technical people who create and maintain the data.
D. Determine which processes should be retired.
35. Which data sampling technique is not valid when conducting an Information Quality
Assessment?
A. Linear Regression
B. Random sampling
C. Cluster sampling
D. Systematic sampling
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36. Which of the following is not a data assessment test?
A. Validity of business rule conformance.
B. Timeliness and Nonduplication.
C. Accuracy to surrogate source including derivation integrity.
D. Usability
37. Which of the following is not a technique for Information Quality Report?
A. Pareto Diagram.
B. Bar Chart.
C. Statistical Control Chart.
D. Business Glossary List.
38. Which one of the following is not one of the redundant costs?
A. Costs of inconsistent data.
B. Cost to capture of interface.
C. Data residing in multiple databases.
D. Data residing in a single sharable database.
39. Which of the following do not determine the cost of data?
A. Cost basis of developing and maintaining infrastructure.
B. Cost to produce a product.
C. Value basis uses the information to add value for the enterprise.
D. Cost to define information requirements and design and build applications and
databases.
40. Which of the following is not a direct result of process failure information costs:
A. Duplicate catalogue mailings to a single customer.
B. Knowledge workers re-verifying the data.
C. Incorrect purchasing decision made.
D. Unhappy supplier trying to make corrections several times through customer
support.
41. What is the relationship between quality information and the business?
A. Quality information costs the enterprise overall.
B. Information enables the enterprise to accomplish its mission and goals.
C. No relation between information and the bottom line.
D. The relation between information and the bottom line cannot be quantified.
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5.3 Information Quality Improvement
42. Where is it best to correct defective data?
A. In downstream databases.
B. Fix the data at the source.
C. Fix the process that produces the defective data by identifying root cause.
D. Mark the defective data through metadata until it can be fixed.
43. What is the process that takes existing data, which is defective and brings the data to
suitable levels of quality?
A. Data Reengineering and cleansing process.
B. Extract Transform and Load (ETL) process.
C. Data Loading process.
D. Metadata process.
44. Which one of the following will Data Architects not consider when embarking on an
Information Product Improvement project?
A. Source Data Cleansing
B. Data Conversions
C. Data Scrubbing
D. Presenting data.
45. An analysis of the data revels that over 60% of the phone numbers in the database are
the same number 000-0000. Further analysis reveals that the phone number cannot be
null. A rule is added to the interface that the phone number cannot be zeros. This is
known as:
A. Source Data Cleansing
B. Data Conversion
C. Data Scrubbing
D. Best Practice
46. In the source to target mapping of data, the data is standardized so that values
depicting gender are the uniform. This is known as:
A. Source Data Cleansing
B. Data Conversion
C. Data Scrubbing
D. Best Practice
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47. What area is considered part of defining summary and derived data, adding data from
external sources, consolidating data?
A. Source Data Cleansing
B. Data Conversion
C. Data Scrubbing
D. Best Practice
48. Which group should conduct the quality audit and control of data movement
procedures?
A. Internal Audit
B. Knowledge Workers
C. Data Conversion Specialists
D. Information Steward
49. Which one of the following is not a cost of information quality?
A. Nonquality information costs.
B. Information quality assessments/audits.
C. Information quality process improvements and prevention.
D. Incorrect decisions made on poor quality information.
50. Which of the following is a proactive technique in Information Process Quality
Improvement?
A. Fix the symptoms.
B. Ignore Problem signs until they become issues.
C. Analyze root cause and eliminate the cause.
D. Following Best Practise in IT System Management
51. Which one of the following techniques is not used in root cause analysis for
determining quality issues?
A. Cause-and-effect diagram
B. Interrelationship diagram
C. Current reality tree
D. Value chain relationship diagram
52. Which step is not involved in the Shewhart cycle?
A. Plan and Do
B. Check
C. Act
D. Refine
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53. When implementing an Information Quality Culture Transformation which one of the
following is most important to do well?
A. Training
B. Management Buy-in
C. Define a Methodology
D. Data Definition quality assessment process
54. Who is accountable for the integrity of the processes and quality of information?
A. Knowledge Steward
B. Managerial Information Steward
C. Process Steward
D. Business Information Steward
55. What is the first step an enterprise should take when embarking on an information
quality program?
A. Conduct an Information Quality Management Maturity Assessment and gap
analysis.
B. Create a vision, mission, and objectives for the information quality program.
C. Appoint an Information Quality Leader.
D. Conduct a customer satisfaction survey.
56. Which state is the least mature in the Information Quality Management Maturity
Assessment?
A. Uncertainty
B. Awakening
C. Enlightenment
D. Wisdom and Certainty
57. Which state is characterized by knowing a data quality problem exists but not
knowing what to do about it?
A. Uncertainty
B. Awakening
C. Enlightenment
D. Wisdom and Certainty
58. Which state has adopted a commitment to quality and implements the 14-point
program?
A. Uncertainty
B. Awakening
C. Enlightenment
D. Wisdom and Certainty
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59. Which state has prevention becoming a normal part of operations?
A. Awakening
B. Enlightenment
C. Wisdom
D. Certainty
60. Which state is the most mature in the Information Quality Management Maturity
Assessment?
A. Awakening
B. Enlightenment
C. Wisdom
D. Certainty
61. When conducting an Information Quality Management Maturity Assessment, what
are the five stages assessed across the Measurement Categories?
A. Management understanding and attitude; Information Quality Organization
Status; Information Quality Problem Handling; Cost of Information Quality as a
Percent of Revenue or Operating Budget; Information Quality Improvement
Actions; and Summary.
B. Plan; Do; Check; Act
C. Definition conformance, completeness, validity, accuracy, precision,
nonduplication, and consistency of redundant data.
D. Accessibility, timeliness, contextual clarity, derivation integrity, usability,
completeness.
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Quick Answers
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Detailed Answers
1. Answer: A. Data is the representation of facts, information is data in context. Data is
the raw material and information is the finished product.
2. Answer: B. Correctness or accuracy of the data and the degree of usefulness and value
data has to the organization. Information Quality needs to have inherent quality (data
accuracy) and pragmatic quality (usefulness and value to support the enterprise process
that enable accomplishing enterprise objectives). Data needs to support the knowledge
workers decision-making process or else it holds no value to the organization.
3. Answer: A. Knowledge Workers. Knowledge Workers benefit the most from
Information Quality because they require data to do their jobs to the benefit of the endcustomer.
4. Answer: D. Zero-defect data. Zero-defect data is required when the consequences of
nonquality cause major process failures. An example of data that must be accurate is
domain reference data like: Medical diagnosis codes.
5. Answer: C. Information = Data + Definition + Presentation. As defined by Larry
English, the three components that make up information are meaning (definition) of a fact
(data) in a context (presentation).
6. Answer: B. The definition, domain value set, and business rules that govern data. Data
Definition quality is the degree to which the data definition describes the meaning of the
data and meets the needs of all stakeholders to understand the data and the context.
7. Answer: A. Information Product Specification. To produce consistent high-quality
information an information product specification is needed. The specification states
clearly and definitely the requirements along with acceptable product variations.
8. Answer: A. Data standards quality, data definition quality and information architecture
quality. Data Definition Quality or “Information Product Specification Quality” is the
specification for building well designed information architecture like manufacturing a
Product.
9. Answer: A. The name of the data plainly conveys the meaning of the objects named.
10. Answer: D. To be consistent with the meaning of actual data values with its data
definition. Definition conformance is comprised of data definition quality, validity and
accuracy.
11. Answer: D. Precision. Precision is the characteristic of having the right level of
granularity in the data values. For example, measurement of uptime of a system(99.999
or 99.5 ) needs to allow for a finer breakdown of values instead of 99% available.
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12. Answer: B. Completeness. Completeness is the characteristic of having all required
values for the data fields. The completeness is measured by the extent of sparsity of the
data. For example, Resident_Age should be a number greater than zero and not null.
13. Answer: C. Accuracy. Accuracy depends on a reliable and precise recording of
information or agreement to original source.
14. Answer: C. Same data maintained in many independent, distributed databases.
Nonduplication represents a one-to-one correlation between a record and the event. An
example of duplication may reside in customer databases in a company where each
department may capture their own customer information.
15. Answer: A. Consistency of Redundant data. When data is semantically equivalent
System A Parent has domain values of (Father, Mother); System B Parent_No (1,2),
System C Parent_Abbr (F, M) all mean the same thing.
16. Answer: B. Information Float is the length of time from when data is known, until it
is available for a specific process or use. The measure of information float is the average
time required for data to be disseminated in the organization. Both concepts are related to
the timeliness: the relative availability of data within the time required by the knowledge
worker. For example, an order may be filled out on an order form and faxed to the
company. Timeliness represents the time taken to enter the order form into the system.
17. Answer: D. It depends on the type of data. Usefulness refers the form of information
presentation and the degree to which a knowledge worker can readily interpret the results.
18. Answer: A. Presentation clarity is the degree the knowledge worker can understand
the meaning of the data through the presentation. Presentation clarity avoids
misinterpretation of the results. May also be referred to as contextual clarity.
19. Answer: D. Objectivity. Objectivity is a statement of fact with a neutral point of
view, reporting without bias, and emphasis on intuitive presentation to the knowledge
worker. Objectivity may also be referred to rightness or fact completeness.
20. Answer: C. Definition conformance, completeness, validity, accuracy, precision,
nonduplication, and consistency of redundant data.
21. Answer: D. Accessibility, timeliness, contextual clarity, derivation integrity, usability,
and completeness.
22. Answer: C. The date that an employee first started with the company regardless of
location. The attribute quality definition describes in business terms the definition of the
attribute that clearly defines one and only one attribute.
23. Answer: B. Service-start-date. Data name and definition consistency measures how
well the name and definition is understood in the organization.
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24. Answer: A. Retail Customer. Retail Customer is the most appropriate to demonstrate
Entity Type Name Clarity. Entity type name clarity is easily understood by the
knowledge worker and represents the objects. The characteristics of Entity Name Type
Clarity are: singular nouns, business terms and easily comprehended by the knowledge
worker.
25. Answer: A. Retail Customer Name. Retail Customer Name is the most appropriate to
demonstrate Attribute Name Clarity. Attribute Name Clarity is easily understood by the
knowledge worker and represents the facts. The characteristics of Attribute Name Clarity
are: business terms, easily associated with the entity type and easily comprehended by
the knowledge worker.
26. Answer: D. Customer. Domain type consistency (also known as class word) in an
attribute represents the type of data stored. For example, Start-Date attribute has a
domain type of date and the valid values would be a subset of all possible dates. Typical
domain types include: date, time, amount, identifier (id), amount, code, name, quantity,
percent, rate, and description.
27. Answer: B. Cross-ref. The most appropriate abbreviation for acronym clarity for the
term cross-reference is Cross-ref. When using abbreviations, they should be documented
in a single, enterprise-wide standards abbreviation list that is used consistently throughout
the enterprise. Rules of thumb for creating abbreviations are: use industry-standards or
universally accepted abbreviations where applicable, use short abbreviations without loss
of meaning, and always use the first letter of the term.
28. Answer: A. Attributes names are consistent where facts context are equivalent.
Names should be appropriate or consistent across the enterprise even across different
formats of presentation and storage formats.
29. Answer: A. Business Steward. A Business Steward in the enterprise is responsible
for keeping the enterprise wide glossary current in an organization. The responsibilities
include adding, changing and deleting the definitions as needed.
30. Answer: D. Subject Matter Expert. A Subject Matter Expert or Business Information
Steward in the enterprise is responsible for keeping the business term definitions current
in an organization. The responsibilities include adding, changing and deleting the
definitions as needed.
31. Answer: A. The business terms should be defined in the glossary that is enterprise
wide. The glossary can take on several forms.
32. Answer: A. Based on technical or existing system limitations. Business rules should
not be due to a technical or existing system limitation. Business rules should be
expressed in business terms, defines an aspect of the business to take action, complete
and specific, plus defines who, what, when, why and how and identities any exceptions.
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33. Answer: B. To measure the quality of data, either in physical form (file, database,
spreadsheet) or output from a process. The information quality assessment is conducted
for the benefit of the knowledge workers.
34. Answer: D. Determine which processes should be retired. The purpose of
information quality assessments is evaluating the processes and data, certify the data, and
providing feedback plus measuring against the baseline to calculate the costs of
nonquality.
35. Answer: A. Linear Regression. Linear Regression is not a valid sampling technique
when conducting an Information Quality assessment.
36. Answer: D. Usability. Usability is not a concern of the data assessment test. Data
assessment tests measure: Validity of business rule conformance, Timeliness,
Nondupliction, Accuracy to surrogate source including derivation integrity, and
Consistency of data.
37. Answer: D. Business Glossary List. The Information Quality Report deduces and
reports on the data assessment using Pareto Diagrams, Bar Chart, Statistical Control
Charts and outputs from Information quality analysis software.
38. Answer: D. Data residing in a single sharable database. The cost of redundancy is
part of the cost formula of information quality in the value basis component. Redundant
costs occur when the data is contained in multiple databases. There is a cost to capture
and control all the multiple databases and of the inconsistent or inaccurate data to the
organization. Data residing in a database has only potential value. Information value
occurs through usage only.
39. Answer: B. Cost to define the interface to acquire customer data. The cost of data is
comprised of two areas: cost basis and value basis. Cost basis is the cost of developing
and maintaining infrastructure. It is made up of the cost to define information
requirements, develop information, application, and technology architectures; and to
design and build applications and databases. Value basis uses the information to add
value for the enterprise.
40. Answer: B. Knowledge workers re-verifying the data. Process failure information
costs result in spent costs, liability and exposure costs, and recovery costs.
41. Answer: B. Information enables the enterprise to accomplish its mission and goals.
The enterprise business performance objectives need to be aligned to measuring
information quality.
42. Answer: C. Fix the process that produces the defective data by identifying root cause.
Data cleansing fixes the data. Information process quality improvements fix the process
that produces the defective data. The process is typically iterative that involves the
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cycles of: planning, implementing, assessing and rollout (Shewhart cycle of Plan-DoCheck-Act or PDCA).
43. Answer: A. Data Reengineering and cleansing process. The process that takes
existing data, which is defective and brings the data to suitable levels of quality, is known
as data reengineering and cleansing. Data reengineering is similar to reverse engineering
but only looks at the data not the application or system. It mainly focuses on how the
data is used in an organization and can work backwards to the data models. Data
reengineering often results in a deeper understanding of data assets of the enterprise and
may lead to areas like: data consolidation, data architecture, and data acquisition
strategies. Data Cleansing is the act of identifying and correcting data. Correcting data
involves cleaning up data that is incorrect, out-of-date, redundant, incomplete, or
formatted incorrectly.
44. Answer: D. Presenting data. Data Architects will consider source data cleansing, data
conversions, and data scrubbing when embarking on an Information Product
Improvement project. These are known as the three data cleansing areas: Source Data
Cleansing, Data Conversions and Data Scrubbing.
45. Answer: A. Source Data Cleansing. Source data cleansing improves existing data
quality where the data is initially stored.
46. Answer: B. Data Conversion. Data conversion is the act of mapping the source to
target and improving the quality of data by correcting, standardizing, de-duplicating,
completing and formatting.
47. Answer: C. Data Scrubbing. Data scrubbing is an act of defining summary and
derived data, adding data from external sources, consolidating data.
48. Answer: D. Information Steward. Information Steward should conduct quality audit
and control of data movement procedures. They should get input from internal audit,
knowledge workers and data conversion specialists.
49. Answer: D. Incorrect decisions made on poor quality information. There are three
categories of information quality costs: Nonquality information costs (process, rework,
lost and missed opportunity costs); Information quality assessments/audits; and
Information quality process improvements and prevention.
50. Answer: C. Analyze root cause and eliminate the cause. Information Process Quality
Improvement has two elemental processes: reactive and proactive. Proactive process
involves conducting root cause analysis. Root Cause Analysis identifies not only what
and how an event occurred, but also why it happened. Only when an analysis of why an
event or failure occurred will corrective measures are found.
51. Answer: D: Value chain relationship diagram. Value chain relationship diagram is
not a type of root cause analysis technique. Cause-and-effect diagram (Ishikawa or
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fishbone diagram) breaks down causes into detailed categories so they can be organized
into related factors to identify root cause. Interrelationship diagram quantifies the
relationships between factors and classifies causes leading to root cause. Current reality
tree classifies interdependent relationships between effects leading to the determination
of root cause.
52. Answer: D. Refine. The Shewhart cycle of Plan-Do-Check-Act or PDCA because
known as the Deming cycle, is the foundation to improve information process quality.
53. Answer: A. Training. Training is most essential when implementing an Information
Quality Culture Transformation for both management and staff. The training needs to
cover why quality is fundamental to the enterprise and how to achieve quality. When
defining training requirements, identify the role and their responsibilities toward
information quality for example: general information, policies and processes, usage, and
information management principles. Next define their training requirements and learning
objectives for each role.
54. Answer: B. Managerial Information Steward. The Managerial Information Steward is
accountable for the integrity of the processes and quality of information. Knowledge
steward is accountable for the use of information. Process Steward is accountable for the
definition of a business process. Business Information is accountable for validating the
definition of data.
55. Answer: A. Conduct an Information Quality Management Maturity Assessment and
gap analysis. When embarking on an information quality program, the first step an
enterprise should take is to conduct an Information Quality Management Maturity
Assessment and gap analysis to determine the current state of the organization and where
they would like to be in the future.
56. Answer: A. Uncertainty. The Uncertainty state is Stage 1 and the least mature in the
Information Quality Management Maturity Assessment. In the Uncertainty stage,
Information quality is not considered a management tool. When issues occur they are
dealt with in a reactive manner.
57. Answer: B. Awakening. The Awakening state is Stage 2 and is characterized by
knowing a data quality problem exists but not knowing what to do about it in the
Information Quality Management Maturity Assessment. In the Awakening stage,
Information quality issues have been identified but management does not commit to their
resolution. When issues occur they are cleaned up rather than fixed at the source and
dealt with in a tight scope.
58. Answer: C. Enlightenment. The Enlightenment state is Stage 3 and is characterized
by adopting a commitment to quality and implementing the 14-point program in the
Information Quality Management Maturity Assessment. In the Enlightenment stage,
Quality Improvement Program is implemented with communication and resolution.
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59. Answer: C. Wisdom. The Wisdom state is Stage 4 and is characterized by
implemented a program of prevention in the Information Quality Management Maturity
Assessment. In the Wisdom stage, Quality Improvement Program is mastered with
quality being integrated into all areas and a routine part of enterprise.
60. Answer: D. Certainty. The Certainty state is Stage 5 and is the most mature in the
Information Quality Management Maturity Assessment. In the Wisdom stage, Quality
Improvement Program is an essential part of the enterprise.
61. Answer: A. Management understanding and attitude; Information Quality
Organization Status; Information Quality Problem Handling; Cost of Information Quality
as a Percent of Revenue or Operating Budget; Information Quality Improvement Actions;
and Summary. To conduct an Information Quality Management Maturity Assessment,
the five stages are assessed across the following Measurement Categories: Management
understanding and attitude; Information Quality Organization Status(maturity of
information quality in the enterprise); Information Quality Problem Handling (acts or
reacts to issues); Cost of Information Quality as a Percent of Revenue or Operating
Budget; Information Quality Improvement Actions; and Summary. Capability can be
quantified using measurable criteria and is realized through an evolutionary not
revolutionary process.
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Selected Bibliography
Brackett, Michael, DATA SHARING: USING A COMMON DATA ARCHITECTURE,
John Wiley, 1994, ISBN 04711309931.
DAMA International & DAMA Chicago Standards Committee, “DATA
MANAGEMENT ASSOCIATION: GUIDELINES TO IMPLEMENTING DATA
RESOURCE MANAGEMENT, DAMA International, 4th edition, 2002.
English, Larry P., IMPROVING DATA WAREHOUSE AND BUSINESS
INFORMATION QUALITY, John Wiley & Sons, 1999, ISBN: 0471253839.
Fowler, Martin, UML DISTILLED: APPLYING THE STANDARD OBJECT
MODELING LANGUAGE, Addison-Wesley, 1997, ISBN: 0-201-32563-2.
Inmon, W.H. BUILDING THE DATA WAREHOUSE, John Wiley, 2002, ISBN 0-471081302.
Kimball, Ralph, THE DATA WAREHOUSE TOOLKIT, John Wiley, 1996, ISBN: 0471-15337-0.
Marco, David, BUILDING AND MANAGING THE META DATA REPOSITORY: A
FULL LIFECYCLE GUIDE, John Wiley, 2002, ISBN: 0471355232.
McFadden, Fred R., Hoffer, Jeffrey A., and Prescott, Mary B. MODERN DATABASE
MANAGEMENT, Fifth Edition, Addison-Wesley, 1999, ISBN 0-8053-6054-9.
Simsion, Graeme and Witt, Graham, DATA MODELING ESSENTIALS, Third Edition,
Morgan Kaufman, 2004, ISBN: 0126445516.
Tannenbaum, Adrienne, METADATA SOLUTIONS: USING METAMODELS,
REPOSITORIES, XML, AND ENTERPRISE PORTALS TO ACHIEVE
INFORMATION ON DEMAND, Addison-Wesley, 2001, ISBN: 0201719762.
Watson, Richard T. DATA MANAGEMENT: DATABASES AND ORGANIZATIONS,
John Wiley & Sons, 2002, ISBN 0-471-41845-5.
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