Free Practice Questions for Pegasystems PEGACPDS88V1 Exam

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.

When building a model using Pega machine learning, the validation hold-out set is used to train the models and to check for robustness of candidate models.

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

According to the Pega Academy1, a strategy is a unit of reasoning that defines how to select an action for a customer. A strategy can be defined in different issues and groups, which are categories that help organize actions. An issue represents a business goal (such as retention or sales), and a group represents a subcategory of an issue (such as cross-sell or up-sell).

In a strategy defined in the “Retention” issue and the “X-Sell” group, you can import actions from all groups under the “Retention” issue1. This allows you to use actions that are relevant to your business goal and compare them with other actions in the same issue.

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

To offer a 10% discount for customers whose CLV value is higher than 400, you should use the Filter strategy component.

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.

In an adaptive model, you can use Symbolic predictors.

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 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