PEGACPDC88V1 Exam Questions & Answers

To select the best offer from both groups based on customer behavior, you need to ensure that the propensity is enabled in Arbitration tab. Propensity is a measure of how likely a customer is to accept an offer, based on their past behavior and profile. By enabling propensity in Arbitration tab, you can compare the propensities of different offers across groups and select the one with the highest propensity as the next best action. Verified Reference:Pega Academy - Decisioning Consultant - Arbitrating actions

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

Pega Customer Decision Hub is a dynamic and adaptive system that constantly reevaluates the Next-Best-Action for each customer based on their interactions and feedback. If a customer rejects an offer, the system will update the customer profile and the offer performance, and then reapply the Next-Best-Action strategy to select and prioritize another offer that is more relevant and valuable for the customer. Verified Reference: [Pega Decisioning Consultant | Pega Academy]

The always-on outbound customer engagement paradigm relies on AI to select the best action for each customer at any given time, based on their profile, context, and behavior. You do not need to create separate segments or schedules for different offers or timings. You can configure the primary schedule to run daily and let the AI choose the best action based on engagement policies, such as contact policies, eligibility rules, suitability rules, and arbitration. The AI will also learn from the customer responses and optimize the action selection over time.

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.