PEGACPDC88V1 Certified Pega Decisioning Consultant 8.8 V1 Question and 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 References: Pega Academy - Decisioning Consultant - Arbitrating actions

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.

Increasing stage upweighting is a feature that allows you to gradually increase the weight of a stage over time, making the offers in that stage more likely to be selected. This is useful for promoting offers that are time-sensitive or have a limited availability. In this case, the bank wants to actively increase offers promotion over time, so enabling increasing stage upweighting for the second stage of the journey, where the offers are presented, is the best option. Verified References: [Pega Decisioning Consultant | Pega Academy]

Volume constraints allow you to limit the number of times an action is presented to customers across one or more channels. You can use volume constraints to implement a policy that restricts the number of emails sent per day. Outbound channel limits are used to limit the number of customers contacted per channel per run, not per day. Suppression rules are used to exclude customers from receiving an action based on certain conditions, such as opt-out preferences or recent purchases. 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 the action is presented. Verified References: Certified Pega Decisioning Consultant | Pega Academy, Volume constraints

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 priority communication is a type of outbound interaction that allows you to send urgent messages to customers that are not related to any specific offer or proposition. You can use priority communications to inform customers about important events or changes that affect their relationship with your organization. In this case, the bank wants to share the new temporary contact details with all customers over an SMS, so creating a priority communication is the best option. Verified References: [Pega Decisioning Consultant | Pega Academy]

The Next-Best-Action strategy is a customer-centric approach that aims to deliver the most relevant and valuable proposition for each customer at any given moment. You can use the Next-Best-Action strategy to address various business issues that involve customer interactions, such as service, retention, and collections. Service is the process of providing assistance and support to customers who have questions or problems. Retention is the process of preventing customers from leaving or switching to competitors. Collections is the process of recovering unpaid debts from customers who are delinquent or defaulting on their obligations. These are all business issues that can benefit from using the Next-Best-Action strategy. Verified References: [Pega Decisioning Consultant | Pega Academy]

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 References: [Pega Decisioning Consultant | Pega Academy]

To test the visualization and the rendering of the email content, including replacing of the placeholders with customer information, you use a seed list from the Test email tab. A seed list is a predefined set of customers that you can use to test your email treatments before sending them to your target audience. You can select one or more customers from the seed list and send them a test email with your treatment. You can then verify how the email looks in their inbox and how the placeholders are replaced with their actual values. Verified References: [Pega Academy - Decisioning Consultant - Testing email treatments]

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

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.