PEGAPCDC87V1 Exam Dumps : Certified Pega Decisioning Consultant (PCDC) 87V1

To define a contact policy that suppresses an action for 20 days if it was rejected twice in any channel in the last 30 days, you need to specify the suppression rule as follows: "Suppress an action for 20 days if there are 2 rejects for any channel in the last 30 days." This rule ensures that the action is paused after it has been rejected a specified number of times across any channel within the given timeframe.

Correct Engagement Policy Configuration:

The customer must not be flagged as a defaulted borrower

Engagement policy: Eligibility

Customer already owns a higher-value offer

Engagement policy: Applicability

Must be a resident of New York City

Engagement policy: Eligibility

The savings product offers are relevant only if the customer has not explicitly opted out of direct marketing

Engagement policy: Applicability

Inappropriate for customers with a credit score < 300 as they are likely to default

Engagement policy: Suitability

The customer must not be flagged as a defaulted borrower

Eligibility: Eligibility conditions determine whether a customer qualifies for an offer at a basic level. Being flagged as a defaulted borrower is a fundamental criterion to disqualify a customer from receiving certain offers.

 Requirement:

U+ wants to show a retention offer to customers likely to leave the bank based on historical interaction data.

 Model Types:

Entity Model: Used for recognizing entities within data.

Text Analytics Model: Used for analyzing text data.

Predictive Model: Used for making predictions based on historical data.

Adaptive Model: Used for real-time adaptation based on new data.

 Suitable Model:

For predicting customer churn (likelihood of leaving), a predictive model is best suited because it leverages historical data to make future predictions.

 Verification from Pega Documentation:

Pega documentation on leveraging predictive models for customer retention and churn analysis.