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  3. PEGACPDS88V1

Pegasystems PEGACPDS88V1 Exam (page: 4)
Pegasystems Certified Pega Data Scientist 8.8
Updated on: 25-Dec-2025

Viewing Page 4 of 29
PEGACPDS88V1 PDF 140 Questions

Proactive retention is applicable when a customer is

  1. Initiating contact to churn
  2. A high value customer
  3. In a collections process
  4. Likely to churn

Answer(s): D

Explanation:

Proactive retention is applicable when a customer is likely to churn. Proactive retention is a strategy that aims to prevent customer attrition by identifying customers who are at risk of leaving and offering them incentives or solutions to retain them. Proactive retention requires predicting the customer's churn risk and selecting the next best action accordingly.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning- /decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm



Pega machine learning supports the creation of which two distinct types of predictive models? (Choose Two)

  1. Categorical
  2. Continuous
  3. Binary
  4. Numerical

Answer(s): A,C

Explanation:

Pega machine learning supports the creation of two distinct types of predictive models: categorical and binary. Categorical models predict the outcome of a variable that can have multiple values, such as product category or customer segment. Binary models predict the outcome of a variable that can have only two values, such as yes or no, accept or reject, etc.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule- decision-/rule-decision-predictivemodel/main.htm



Which decision component enables you to use a PMML model?

  1. Predictive Model
  2. PMML Model
  3. Third-party Model
  4. Adaptive Model

Answer(s): A

Explanation:

The decision component that enables you to use a PMML model is Predictive Model. Predictive Model is a component that references a predictive model rule that defines the input parameters and the output score of the model. You can use a predictive model component to reference a PMML model that is imported from a third-party tool and use it in your decision strategy.


Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule- decision-/rule-decision-predictivemodel/main.htm



The standardized model operations process (MLOps) lets you replace a low-performing predictive model that drives a prediction with a new one.
Which feature of MLOps lets you monitor the new model in the production environment without affecting the business outcomes?

  1. Change request
  2. Shadow mode
  3. Historical data capture
  4. Connection to machine learning services

Answer(s): B

Explanation:

This is because shadow mode allows you to test a new model in parallel with an existing model without affecting the decision outcomes. You can compare the performance of both models and decide whether to replace or keep the existing model.

https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN- StudentGuide.pdf



A large online store uses Pega Customer Decision Hub to smoothly adapt to changing customer behavior. Adaptive models help accomplish this business objective as the models learn from customer responses.
Which statement about adaptive models is correct? s

  1. Adaptive models perform a binary model calculation
  2. Adaptive models require underlying predictive models
  3. Adaptive models require a historical data set to start learning
  4. Adaptive models perform a continuous model calculation

Answer(s): A

Explanation:

Adaptive models perform a binary model calculation. This means that adaptive models predict the likelihood of a positive or negative response for each action and customer profile. Adaptive models do not require underlying predictive models or historical data sets to start learning. They learn from customer responses in real time and continuously update their predictions.


Reference:

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



Viewing Page 4 of 29



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