Free Pegasystems Certified Pega Decisioning Consultant 25 PEGACPDC25V1 Exam Questions

An engagement policy condition is a rule that determines whether a customer is eligible, applicable, or suitable for an offer. You can use engagement policy conditions to filter customers based on their attributes or behaviors. In this case, the bank wants to offer a Gold credit card to customers who have an annual income of more than USD150000, so creating an engagement policy condition based on the income attribute is the best option. 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

To implement this requirement, you need to create an external email treatment and use an external tool to design the email. An external email treatment is a type of treatment that allows you to use a third-party email service provider to send emails to your customers. You can configure the external email treatment with the name of the provider, the authentication details, and the template ID that corresponds to the email content that you design using an external tool. This way, you can leverage your existing tools and processes for sending emails, while still using Customer Decision Hub to select the best actions and treatments for your customers. Verified Reference:Pega Academy - Decisioning Consultant - Creating external treatments

To present offers from the two groups, you must map a real-time container to the Top-level or Issue-level. A real-time container is a configuration that defines how to deliver offers and treatments to a specific channel, such as a website or a mobile app. By mapping a real-time container to the Top-level or Issue-level, you can enable all the offers under that level to be available for delivery through that channel. Verified Reference:Pega Academy - Decisioning Consultant - Configuring real-time containers

The score calculation is independent of the strategy and no change is required. When you use a scorecard component in a decision strategy, you only need to specify the name of the scorecard rule and the output property that will store the score value. The scorecard rule itself defines how the score is calculated based on the input properties and factors. Therefore, if you update the scorecard rule to include a new property in the calculation, you do not need to make any changes in the decision strategy for the updated scorecard to take effect. Verified Reference: [Pega Academy - Decisioning Consultant - Using scorecards]

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.