Free Pegasystems PEGACPDS88V1 Practice Exam with Questions & Answers | Set: 2

In an adaptive model, you can use Symbolic predictors.

To assess the general health of the adaptive models in Prediction Studio, you can inspect the performance of the models and the number of decisions. The performance of the models shows how well they predict customer behavior over time. The number of decisions shows how much data is available for each model to learn from. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/monitoring-adaptive-models

To predict if a customer is likely to churn, you use a model of type decision tree. A decision tree is a type of predictive model that uses a set of rules to classify customers into different categories based on their attributes and behavior. A decision tree can predict a binary outcome (such as churn or not churn) or a multi-class outcome (such as low risk, medium risk, or high risk). References: https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models

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.

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 Predictive Model Markup Language (PMML) allows for predictive models to be easily shared between applications. PMML is a standard XML format that describes the input parameters, output score, and mathematical formulas of predictive models. PMML enables interoperability between different tools and platforms that support PMML, such as Pega Customer Decision Hub. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-predictivemodel-/data-predictivemodel-pmml/main.htm

An adaptive model instance is created when you execute a strategy containing the adaptive model component. The adaptive model component references an adaptive model rule that defines the predictors and the outcome of the model. The adaptive model instance stores the data and the statistics of the model for a specific context and action. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

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.

 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

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