Free Practice Questions for Pegasystems PEGACPDS88V1 Exam

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

Adaptive model predictors are selected from the customer profile, which contains information about the customer’s attributes and behavior. Predictors can be either scalar or aggregate properties that capture customer context, such as channel, location, time, etc. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/configuring-adaptive-models

In this scenario, the system creates an adaptive model for each case type, such as claim or complaint. The adaptive model learns from the outcomes of each case type and predicts the probability of case completion for each customer. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-case-management

 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

Positive and negative behavior Reference:

To configure an adaptive model, you must identify positive and negative behavior that indicate specific customer behavior.

Call center, SMS, and Retail store Reference:

MyCo can use Call center, SMS, and Retail store as real-time channels to present Next-Best-Actions.

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

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

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

Pega Process AI™ lets you bring your own predictive models to Pega and use predictions in case types to optimize the way your application processes work and meet your business goals.

To use the outcome of a predictive fraud model in the case type that processes the incoming claim, you need to use the model outcome in the condition of a decision step2. This way, you can route suspicious claims to a fraud expert for closer inspection based on the model’s prediction.