Pegasystems Certified Pega Data Scientist 8.8 PEGACPDS88V1 Exam Dumps: Updated Questions & Answers (October 2025)

The Switch component allows you to use a third-party Credit Risk Model 80% of the time and a Pega Credit Risk Model 20%.

Up to 100 fields to limit the impact on model speed Reference:

When configuring an adaptive model, it is recommended to select up to 100 potential predictors to limit the impact on model speed.

Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub™ predictions are using a control group and selecting the model with the highest performance.

 Through analysis of customer lifecycles, Next-Best-Action anticipates retention issues and takes proactive actions to prevent customer churn. 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

When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.

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. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-adm-/data-adm-model/main.htm

 The most important aspects taken into consideration when determining the Next-Best-Action are business objectives and customer needs. Business objectives reflect the goals and priorities of the organization, such as increasing revenue, reducing costs, or managing risk. Customer needs reflect the preferences and expectations of the customers, such as their interests, intents, or life events. References: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

Pega Customer Decision Hub™ uses the PCV*L arbitration formula to select the next best action for each customer. The purpose of the formula is to balance customer needs with business objectives.

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

The expected performance of a binary model must be set before the model can be deployed.