Free Pegasystems PEGACPDS88V1 Practice Exam with Questions & Answers | Set: 3

The likelihood that a proposition will be accepted by the customer is stored in the strategy property called pyBehavior. This property is calculated by an adaptive model or a predictive model and reflects the customer’s propensity to respond to an offer. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/using-predictions-decision

To comply with the new business rule of showing a proactive retention offer to high churn risk customers, you should modify the Retention issue.

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

Adaptive models can start to learn without historical evidence. The starting propensity of every action is 0.5.

As a data scientist, if you are tasked with configuring two predictions that are driven by an adaptive model, you need to pay extra attention to adaptive model settings.

You can use one Set Property component to calculate the average margin of four actions by using an expression that sums up the margin values of each action and divides by four. You can then use this property in other components, such as Filter or Prioritize. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties

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

A predictive model is a mathematical function that estimates the probability of an outcome based on input data. When building a predictive model, the use of testing and validation samples increases the accuracy of models123. Testing and validation samples are subsets of data that are used to evaluate how well a model performs on new data that was not used to train the model. Testing and validation samples help prevent overfitting, which is when a model learns too much from the training data and fails to generalize to new data.

According to the Pega Academy1, decision strategies drive the next best action and comprise a unit of reasoning represented by decision components. You use the Proposition Data component to import actions into a strategy canvas. The sequence of the components in the canvas determines which action is selected for a customer.

The Group By component2 is used to group a list of ranked items based on a field and retain only one element in each group. The function available when configuring the Group By component is Count2, which returns the number of elements in each group.