Pass Pegasystems PEGACPDS88V1 Exam with Real Questions
Pegasystems PEGACPDS88V1 Exam
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1.A Scoring Model allows you to differentiate between
A. Accept, Reject, Maybe Later
B. Good, Bad
C. Good, Better, Best
D. Good, Bad, Unknown
Answer: C
Explanation:
A scoring model allows you to differentiate between Good, Better, and Best outcomes for a given
proposition or action. A scoring model assigns a numerical value to each outcome based on its desirability
or profitability for the business.
References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
2.As a data scientist, you have enabled capturing of historical data in an adaptive model.
Which two data elements are captured for every customer interaction? (Choose Two)
A. The value of only the active predictors
B. The outcome of the interaction
C. The model metadata
D. The propensity generated by the model
E. The value of all predictors
Answer: B,E
Explanation:
When capturing historical data in an adaptive model, the outcome of the interaction and the value of all
predictors are captured for every customer interaction.
3.What is the difference between predictive and adaptive analytics?
A. Predictive models can predict a continuous value.
B. Predictive models predict customer behavior.
C. Adaptive models use the customer data as predict*
D. Predictive models have evidence.
Answer: C
Explanation:
The difference between predictive and adaptive analytics is that adaptive models use the customer data
as predictors, while predictive models use the customer data as outcomes. Adaptive models learn from
real-time customer interactions and update their predictions accordingly. Predictive models use historical
customer data to train and validate their predictions.
References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/ada
ptive-models-overview
4.The outcome of a scoring model indicates the likely
A. write-off value of an arrears case
B. claim value of a health insurance
C. period in which a spare part has to be replaced
D. response to an offer
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Answer: D
Explanation:
The outcome of a scoring model indicates the likely response to an offer that is presented to a customer.
For example, a scoring model can predict if a customer will accept, reject, or defer an offer for a credit
card upgrade.
References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
5.The standardized machine learning process (MLOps) lets you replace a low-performing predictive
model that drives a prediction with an updated model. When you approve the model, a change request is
automatically generated in__________
A. the business operations environment
B. an external environment
C. the production environment
Answer: C
Explanation:
When you approve the updated model in the standardized machine learning process (MLOps), a change
request is automatically generated in the production environment.
6.What is the key difference between a predictive model and a human expert?
A. Predictive models always outperform human experts.
B. Humans are better at dealing with structured data and identifying patterns.
C. Predictive models are more capable of detecting patterns in historical data.
D. Humans make successful predictions on a large amount of data.
Answer: B
7.As a data scientist, you are tasked with configuring two predictions that are driven by an adaptive model:
one for an inbound channel and one for an outbound channel.
To which setting do you need to pay extra attention?
A. Response timeout
B. Adaptive model
C. Predictor fields
D. Control group
Answer: B
Explanation:
As a data scientist, if you are tasked with configuring two predictions that are driven by an adaptive model,
you need to pay extra attention to adaptive model settings.
8.U+ Telecom wants to engage in proactive retention to reduce churn. As a data scientist, you create a
prediction that calculates the probability that a client is likely to cancel a subscription.
What type of prediction do you create?
A. Case management_____
B. Customer Decision Hub
C. Text analytics
Answer: B
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Explanation:
As a data scientist, you create a prediction that calculates the probability that a client is likely to cancel a
subscription. The type of prediction you create is Customer Decision Hub.
9.Which component(s) do you use to calculate the average margin of four actions?
A. one Set Property component
B. one Group By component
C. four Group By components
D. four Set Property components
Answer: A
Explanation:
You can use one Set Property component to calculate the average margin of four actions by using an
expression that sums up the margin values of each action and divides by four. You can then use this
property in other components, such as Filter or Prioritize.
References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting
-properties
10.An adaptive adaptive model component in a decision: propensity, performance, evidence, and
positives.
What is evidence in the context of an adaptive model?
A. The likelihood of a statistically similar behavior
B. The number of customers who exhibited statistically similar behavior
C. The number of statistical bins that arc generated by the system
D. The number of outcomes that system registered
Answer: B
Explanation:
Evidence is the number of customers who exhibited statistically similar behavior. It indicates how much
data the model has collected for a given predictor profile. The higher the evidence, the more reliable the
model is.
References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-de
cision-adaptivemodel/main.htm
11.You are the Decisioning Consultant on an Al-powered one-to-one Customer Engagement
implementation project. You are asked to design the Next-Best-Action prioritization expression that
balances the customer needs with the business objectives.
What factors do you consider in the prioritization expression?
A. product eligibility rules
B. customer contact rules
C. product compatibility rules
D. business levers
Answer: D
Explanation:
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Business levers are factors that you consider in the prioritization expression to balance the customer
needs with the business objectives. They can include revenue, cost, risk, retention, satisfaction, or any
other custom metric that reflects the value of an action.
References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/usingbusiness-levers
12.What type of a predictor can you use in an adaptive model?
A. Symbolic
B. Integer
C. Page Type
D. Logical
Answer: A
Explanation:
In an adaptive model, you can use Symbolic predictors.
13.An online store is interested in increasing its revenues from cross-selling and wants to predict the
acceptance rate of the offers presented on their website. A customer's propensity to accept an offer
increases when_________.
A. Similar offers were rejected by the customer
B. The offer was rejected by similar customers
C. Similar offers were accepted by the customer
D. The offer was accepted by similar customers
Answer: C
Explanation:
This is because a customer’s propensity to accept an offer depends on their past behavior and
preferences. If a customer has accepted similar offers in the past, they are more likely to accept a new
offer that matches their interests
https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuid
e.pdf
14.When implementing a Next-Best-Action project, which step is recommended to be taken first?
A. Define Issue and Group hierarchy
B. Define propositions
C. Define business rules
D. Define prioritization formula
Answer: A
Explanation:
When implementing a Next-Best-Action project, the recommended first step is to define Issue and Group
hierarchy, which are used to organize and categorize propositions based on business objectives and
customer needs. This step helps to align the project with the business vision and goals.
References:
https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer
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