PEGACPDC88V1 Questions and Answers

To enable access to customer properties in a Filter component, you need to use a Data Import component. A Data Import component allows you to read data from various sources, such as data sets, data pages, or data flows, and make it available for other components in the strategy. In this case, you need to use a Data Import component that reads from a customer data source that contains income and age properties. Verified References: Pega Academy - Decisioning Consultant - Importing data

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.

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.

The scorecard model determines the customer credit score based on various factors, such as income, expenses, assets, liabilities, etc. The scorecard model has a Results tab where you can define the cutoff values for different segments based on the credit score. To change the threshold value for the USD2000 credit limit from <175 to <200, you need to change the cutoff value in the Results tab of the scorecard model. Changing the cutoff value in the scorecard decision component, changing the condition in the strategy, or mapping the score value in the decision strategy will not affect the credit score calculation or segmentation. 

Aggregation components are used to perform calculations on a list of actions, such as sum, average, count, minimum, or maximum. For example, you can use an aggregation component to calculate the total value of all the actions in a group. Verified References: Pega Academy - Decisioning Consultant - Aggregating actions

A customer might receive an action that they already accepted if the volume constraint for that action is not configured to exclude previously accepted offers. This option can be enabled by selecting the Exclude previously accepted actions checkbox in the volume constraint configuration. The action suitability conditions are used to determine whether an action is suitable for a customer based on their propensity, priority, or other criteria, not based on their previous responses. The suppression rules are used to exclude customers from receiving an action based on certain conditions, such as opt-out preferences or recent purchases, not based on their previous responses. The actions are filtered based on eligibility before applying the volume constraints, so this option does not explain why a customer might receive an action that they already accepted. Verified References: Certified Pega Decisioning Consultant | Pega Academy, Volume constraints

To output the most profitable shoe, you need to add a Prioritize component in the blank space. A Prioritize component allows you to rank actions based on one or more properties. In this case, you can rank the shoes based on the Profit property and select the highest ranked shoe as the output. Verified References: Pega Academy - Decisioning Consultant - Prioritizing actions

To implement this requirement, you need to select the Refresh the audience checkbox in the outbound run configuration. This option allows you to refresh the audience data before each run by executing a data flow that reads from your customer data source and updates your customer data set. This way, you can ensure that the outbound run always uses the latest customer information available in your system. Verified References: Pega Academy - Decisioning Consultant - Configuring outbound runs

According to the best practice, a next-best-action designer should modify the decision strategy in Customer Decision Hub, because the decision strategy is part of the next-best-action framework that defines how to select and prioritize propositions for each customer. The 1:1 Operations Manager portal is used to manage the engagement policies and propositions, not the decision strategies. Verified References: Pega Decisioning Consultant | Pega Academy

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 References: Pega Academy - Decisioning Consultant - Using decision tables

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 References: Pega Academy - Decisioning Consultant - Configuring real-time containers