C1000-144 Exam Tips and Tricks:
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IBM C1000-144 Exam Detail
Exam Code
C1000-144
Full Exam Name
IBM Certified Data Scientist - Machine Learning Specialist v1
Number of Questions 61
Practice Exams
IBM C1000-144 Certification Practice Exam
Passing Score
74%
Time Limit
90 mins
Books / Training
IBM Machine Learning Specialist
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How to Prepare for C1000-144 Exam?
• Perform enough practice with IBM system with related IBM C1000-144
certification subjects
• Identify the key configuration, workflow and data flow
• Understand the all Syllabus Topics of Exam.
• Identify your weak areas from practice test and do more practice with
system
• Repeat practice exams and try to score 100% on www.edusum.com
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IBM C1000-144
Exam Questions
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Q 1) What does an R^2 Score (coefficient of determination) measure?
Option.
a) It computes the average squared difference between estimated values
and the actual value.
b) It computes the square root of the average squared difference between
estimated values and the actual value.
c) It represents the proportion of the variance in the independent variable
that has been explained by the dependent variable.
d) It represents the proportion of the variance in the dependent variable
that has been explained by the independent variable(s).
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ANSWER
d) It represents the proportion of the variance in the
dependent variable that has been explained by the
independent variable(s).
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Q 2) K-nearest neighbors and K-means clustering with Euclidean
distance suffer from the curse of dimensionality. Is the above
statement true and why?
Option.
a) This is true. Euclidean distance, in general, is not a good metric in a highdimensional space.
b) This is false. K-nearest neighbors can effectively work with a
highdimension space if K is small.
c) This is true. As K increases, the number of samples required grows
linearly with respect to the dimension and K.
d) This is false. K-means clustering can effectively work with a
highdimension space if given a large number of samples.
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ANSWER
a) This is true. Euclidean distance, in
general, is not a good metric in a highdimensional space.
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Q 3) What is one way that IBM AutoAI helps make it easier for data
scientists to determine what key fields to use to join data tables?
Option.
a) AutoAI automatically suggests joining on fields in different tables that
have matching names.
b) AutoAI automatically suggests joining on fields in different tables that
have matching values.
c) AutoAI automatically suggests joining on fields in different tables when
those tables have the same number of rows.
d) AutoAI automatically suggests joining on fields in different tables when
those tables have the same number of fields.
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ANSWER
a) AutoAI automatically suggests joining on
fields in different tables that have matching
names.
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Q 4) The Bayesian optimization algorithm for hyperparameter tuning
has which trait?
Option.
a) It can be used for both regression and clustering but cannot be used for
classification.
b) It requires that a user provide a list of all of the values that will be tested
for each hyperparameter.
c) It builds a machine learning model to predict which hyperparameter
settings will be most effective.
d) If sufficient hardware is available, it can try out many different
hyperparameter settings at the same time.
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ANSWER
c) It builds a machine learning model to predict
which hyperparameter settings will be most effective.
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Q 5) Which of the following are signs that AI is being successfully
infused into the organization?
(Choose Three)
Option.
a) AI technologies are integrated into key business processes.
b) AI usage is restricted to a small number of specialists.
c) Employees across the organization are trained in AI capabilities.
d) There is a clear governance framework for AI ethics and use.
e) AI projects are often initiated but rarely completed.
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ANSWER
a) AI technologies are integrated into key business processes.
c) Employees across the organization are trained in AI
capabilities.
d) There is a clear governance framework for AI ethics and use.
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