Pegasystems pegacpds88v1 practice test

Certified Pega Data Scientist 8.8

Last exam update: Nov 18 ,2025
Page 1 out of 10. Viewing questions 1-15 out of 140

Question 1

To enable an assessment of its reliability the adaptive model produces four outputs:
propensity,performance, evidence and positives.
The Performance of an adaptive model that has not collected any evidence yet is______.

  • A. 75
  • B. 0
  • C. 50
  • D. 100
Mark Question:
Answer:

C


Explanation:
The performance of an adaptive model that has not collected any evidence yet is 50. This means that
the model is not confident about its predictions and assigns equal probability to all actions.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-
/rule-decision-/rule-decision-adaptivemodel/main.htm

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Question 2

When you create a decision strategy from scratch and want to associate an adaptive model with each
action, you need to_______.

  • A. Capture the customer's response
  • B. Define the adaptive model instances in Adaptive Decision Manager
  • C. Link all actions with an adaptive model decision component
  • D. Create an adaptive model rule for each action
Mark Question:
Answer:

B


Explanation:
To associate an adaptive model with each action in a decision strategy created from scratch, you
must define the adaptive model instances in the Adaptive Decision Manager.

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Question 3

What are two of the results of an adaptive model? (choose two)

  • A. Performance
  • B. Evidence
  • C. Priority
  • D. Segment
Mark Question:
Answer:

A,B


Explanation:
Performance and evidence are two of the results of an adaptive model. Performance is the
percentage of positive responses that the model predicts for a given predictor profile. Evidence is the
number
of
customers
who
exhibited
statistically
similar
behavior.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 4

The adaptive model component in a decision strategy computes

  • A. A single propensity value for all actions
  • B. A unique accept rate for each action
  • C. A single accept rate for all actions
  • D. A propensity value for each action
Mark Question:
Answer:

D


Explanation:
The adaptive model component in a decision strategy computes a propensity value for each action.
Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges
from
to
100.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 5

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
Mark Question:
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.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 6

Which adaptive model output is automatically mapped to a strategy property?

  • A. Performance
  • B. Score
  • C. Propensity
  • D. Evidence
Mark Question:
Answer:

C


Explanation:
Propensity is the adaptive model output that is automatically mapped to a strategy property.
Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges
from
to
100.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 7

When selecting the list of predictors for an adaptive model you should

  • A. Select up to a maximum of 500 predictors
  • B. Consider properties from a wide range of sources
  • C. Always use numeric type for integer properties
  • D. Select at least one date property
Mark Question:
Answer:

B


Explanation:
When selecting the list of predictors for an adaptive model you should consider properties from a
wide range of sources. Predictors are properties that influence the customer behavior and can be
derived from various sources such as customer profile, interaction history, proposition details, etc.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-
/rule-decision-/rule-decision-adaptivemodel/main.htm

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Question 8

What happens when you increase the performance threshold setting of an adaptive model rule?

  • A. The number of active predictors increases
  • B. The performance of the model is increased
  • C. The correlation threshold decreases
  • D. The number of active predictors may decrease
Mark Question:
Answer:

D


Explanation:
When you increase the performance threshold setting of an adaptive model rule, the number of
active predictors may decrease. The performance threshold is the minimum performance that a
predictor must have to be included in the model. If you increase this value, some predictors may not
meet
the
criteria
and
be
excluded
from
the
model.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 9

Which two factors do you inspect to access the general health of the adaptive models in Prediction
Studio? (Choose Two)

  • A. Performance of the models
  • B. Number of responses
  • C. Number of decisions
  • D. Model transparency
Mark Question:
Answer:

A,D


Explanation:
These factors indicate how accurate and explainable the models are, which are key measures of
model health. The number of responses and decisions are related more to model usage rather than
health.

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Question 10

Lift is key metric for the performance of the adaptive models.
To measure lift, you need a____________

  • A. Standard model as a benchmark
  • B. Control group with random actions
  • C. Control group with a new action
  • D. New adaptive model
Mark Question:
Answer:

B


Explanation:
Lift is a key metric for the performance of the adaptive models. To measure lift, you need a control
group with random actions. Lift is the ratio of the performance of the adaptive model to the
performance of the control group. A control group is a subset of customers who receive random
actions
instead
of
the
ones
suggested
by
the
model.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-adm-
/data-adm-model/main.htm

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Question 11

Adaptive model components can output__________

  • A. An option___________
  • B. An optimized strategy
  • C. The number of customer's eligible for an action
  • D. The customer's propensity to accept an action
Mark Question:
Answer:

D


Explanation:
Adaptive model components can output the customer’s propensity to accept an action. Propensity is
the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-
/rule-decision-/rule-decision-adaptivemodel/main.htm

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Question 12

An adaptive model instance is created when you________

  • A. Execute a strategy containing the adaptive model component
  • B. Open the Adaptive model management landing page
  • C. Restart the Adaptive Decision Manager Service
  • D. Save the Adaptive model rule
Mark Question:
Answer:

A


Explanation:
An adaptive model instance is created when you execute a strategy containing the adaptive model
component. The adaptive model component references an adaptive model rule that defines the
predictors and the outcome of the model. The adaptive model instance stores the data and the
statistics
of
the
model
for
a
specific
context
and
action.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 13

Which data is usually not appropriate to be used as a predictor?

  • A. Customer zip code
  • B. Historical interaction data
  • C. Customer name
  • D. Usage data
Mark Question:
Answer:

C


Explanation:
Customer name is usually not appropriate to be used as a predictor. A predictor is a property that
influences the customer behavior and can be derived from various sources such as customer profile,
interaction history, proposition details, etc. Customer name is not likely to have any impact on the
customer’s preferences or responses, and it may also violate privacy regulations. Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm

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Question 14

Which statement about predictive models is true?

  • A. Predictive models need historical data to be created
  • B. Predictive models need to be specified in a data attribute
  • C. Predictive models are always associated with an action
  • D. Predictive models need unstructured bie data
Mark Question:
Answer:

A


Explanation:
Predictive models need historical data to be created. Predictive models are statistical models that
use historical data to learn patterns and trends and make predictions for future outcomes. Predictive
models can be built with Pega machine learning or imported from third-party tools such as PMML or
H2O.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm

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Question 15

The use of an imported third-party model in a decision strategy is____

  • A. Only possible after conversion into a Pega machine learning model
  • B. Identical to the use of an adaptive model
  • C. Similar to the use of a model built with Pega machine learning
  • D. Only possible after conversion into Pega markup language
Mark Question:
Answer:

C


Explanation:
The use of an imported third-party model in a decision strategy is similar to the use of a model built
with Pega machine learning. You can use a predictive model component in a decision strategy to
reference an imported third-party model and pass the input parameters and receive the output
score. You do not need to convert the third-party model into a Pega machine learning model or Pega
markup
language.
Reference:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm

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