Pegasystems PEGACPDC88V1 Dumps - Pass Certified Pega Decisioning Consultant 8.8 Exam in 2026

To implement the business requirement, you need to add a decision table to a decision strategy and pass the credit score as the parameter. A decision table allows you to define rules based on one or more input parameters and return an output value. In this case, you can use the credit score as an input parameter and return the risk category/grade as an output value. You can then use this output value to filter out customers who are not in the low-risk segment (AAA). Verified Reference:Pega Academy - Decisioning Consultant - Using decision tables

The prioritization expression is a formula that calculates the priority score of each offer for each customer, based on various factors that reflect the customer needs and the business objectives. One of the most important factors is the predicted customer behavior, which is measured by the propensity. The propensity is a value that indicates how likely a customer is to accept an offer, based on their attributes and behaviors. The propensity is calculated by using predictive analytics models that learn from historical data and feedback. The higher the propensity, the higher the priority score, making the offer more relevant and valuable for the customer. Verified Reference: [Pega Decisioning Consultant | Pega Academy]

To update the cashback offer, you need to edit the engagement policy and the action details. Editing the engagement policy allows you to add a new channel (SMS) and update the suitability condition (lowering the debt-to-income ratio). Editing the action details allows you to specify the treatment for each channel (web and SMS). Verified Reference: [Pega Decisioning Consultant | Pega Academy]

A suppression policy allows you to define conditions that prevent customers from receiving an action or a group of actions. You can use a suppression policy to implement the requirement that customers do not see home loan offers on their account page if they have already received three home loan offers in the last two weeks. You can configure the suppression policy to suppress the home loan group based on the number of times the customer received any action from that group in the past 14 days. Applicability rules are used to determine whether an action is relevant for a customer based on their profile or context, not based on the number of times they received an action. Customer contact limits are used to limit the number of times a customer can be contacted per channel per time period, not based on the number of times they received an action. Volume constraints are used to limit the number of times an action is presented to customers across one or more channels, not based on the number of times they received an action. Verified Reference: [Certified Pega Decisioning Consultant | Pega Academy], Suppression policies

Using the decision table, you can find the label for a customer with a credit score of 240 and an average balance of 35000 by following these steps:

Start from the top row and check if the customer's credit score is less than 150. If yes, then the label is Very Poor. If no, then move to the next row.

Check if the customer's credit score is less than 175 and their average balance is less than 25000. If yes, then the label is Poor. If no, then move to the next row.

Check if the customer's credit score is less than 200 and their average balance is less than 50000. If yes, then the label is Fair. If no, then move to the next row.

Check if the customer's credit score is less than 250 and their average balance is less than 75000. If yes, then the label is Good. If no, then move to the last row.

The last row applies to all other cases that do not match any of the previous conditions. The label for this row is Very Poor.

In this case, the customer's credit score is not less than 150, so the first row does not apply. The customer's credit score is less than 175, but their average balance is not less than 25000, so the second row does not apply either. The customer's credit score is not less than 200, so the third row does not apply. The customer's credit score is less than 250 and their average balance is less than 75000, so the fourth row applies. Therefore, the label for this customer is Poor.