PEGACPDS88V1 Pegasystems Certified Pega Data Scientist 8.8 Free Practice Exam Questions (2025 Updated)

According to the Data Scientist Student Guide1, page 18, the correct answer is B. A predictive model and C. A control group. A predictive model is a mathematical representation of a real-world process that can be used to predict an outcome based on input data. A control group is a subset of customers who are not exposed to a treatment (such as an offer) and are used to measure the effectiveness of the treatment by comparing their behavior with the treated group.

A solid arrow from a “Set Property” component to a “Filter” component means that a property from the “Set Property” component is referenced by the “Filter” component. For example, you can use a “Set Property” component to calculate a customer’s age and then use a “Filter” component to remove actions that are not suitable for that age group. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties

The Customer Lifetime Value Threshold is typically used to prioritize customers based on their expected long-term value to the business. Customers who have a higher lifetime value than the threshold are considered high-value customers and receive more personalized and relevant offers. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

A scorecard model is a type of predictive model that allows you to monitor the real-time performance of a third-party churn model. A scorecard model compares the predicted churn probability with the actual churn outcome and calculates a performance score for each customer segment. References: https://academy.pega.com/module/predictive-analytics/topic/using-scorecard-models

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-StudentGuide.pdf

 The use of testing and validation samples increases the accuracy of predictive models by allowing you to evaluate how well they perform on unseen data and avoid overfitting or underfitting problems. References: https://academy.pega.com/module/predictive-analytics/topic/creating-predictive-models

The new model does not affect business outcomes Reference:

When you place a new predictive model in shadow mode, it does not affect business outcomes.

When performing regular inspection of adaptive models, two issues that should be discussed with the business are predictors with a low performance and actions that are offered so often that they dominate other actions.

The adaptive model output that is automatically mapped to a strategy property is propensity, which indicates the likelihood that the customer will accept or respond to an offer. Propensity is also known as behavior or probability in decision strategies. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/using-adaptive-models-decision

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/adaptive-models-overview

to create a prediction that calculates the probability that a claim is fraudulent, you need to create a case management prediction. This type of prediction allows you to use predictive models built on external platforms such as H2O.ai and apply them to case types in Pega Process AI™. You can then use the prediction outcome in a decision step to route claims based on their fraud risk.

https://academy.pega.com/challenge/creating-fraud-prediction/v3

 The implementation of Next-Best-Action must involve defining business issue and group hierarchy, which are used to organize and categorize propositions based on business objectives and customer needs. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

The mapping of the input fields of a third-party predictive model is done in the Predictive Model rule. The Predictive Model rule defines how to invoke and interpret the results of a third-party predictive model that is imported in PMML format. References: https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income Reference:

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income require no preprocessing before being used as potential predictors in adaptive analytics.

The purpose of model templates when using Pega machine learning is to streamline model development.

Proactive retention is a strategy that anticipates potential customer churn and takes actions to prevent it before it happens. It uses predictive analytics to identify customers who are at risk of leaving and offers them incentives or solutions to retain them. References: https://academy.pega.com/module/one-one-customer-engagement/topic/proactive-retention

The Monte Carlo method is a way to create a data set that includes mock-up customer data. You can use this method to generate random values for customer properties, such as age, gender, income, etc., based on predefined probabilities and distributions. References: https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

The purpose of predictions is to build adaptive models.