PEGACPDC88V1 Certified Pega Decisioning Consultant 8.8 V1 Questions and Answers

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 References: Pega Academy - Decisioning Consultant - Creating external treatments

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.

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

To access a property from an unconnected component, you use the component-dot-property construct. For example, if you want to access the property .Rank from an unconnected component named ActionRanking, you use ActionRanking.Rank. Verified References: Pega Academy - Decisioning Consultant - Accessing properties from unconnected components

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 propensity is a measure of 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. A low propensity value indicates that the offer is not relevant or attractive for the customer, and that similar customers have ignored or rejected the offer in the past. Therefore, if the offer click-through propensity decreased to 0.42, it most likely indicates that similar customers ignore the offer. Verified References: Pega Decisioning Consultant | Pega Academy

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]

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In a decision strategy, to use a customer property in an expression, you need to define Customer page in Pages & Classes and specify its class as Data-Customer. This allows you to access customer properties by using dot notation, such as Customer.Age or Customer.Gender. You do not need to define the property as a strategy property, use it without any prefix, or prefix it with the keyword Customer. Verified References: [Certified Pega Decisioning Consultant | Pega Academy], Decision strategies

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 References: [Pega Academy - Decisioning Consultant - Using scorecards]

The outbound run delivers 75 emails and 25 SMSes for the Standard card offer because the volume constraint is set to limit the number of actions per channel per day. The email channel has a limit of 75 actions per day, so all 75 email offers are delivered. The SMS channel has a limit of 25 actions per day, so only 25 SMS offers are delivered. The remaining 50 SMS offers are not delivered because they exceed the volume constraint.

Paul cannot see the Reward card offer because he rejected other credit card offers twice on the web channel and once in contact center in the past 15 days. This triggers the first contact policy that suppresses a group of credit card offers for 30 days if any credit card offer is rejected three times in any channel in the past 15 days. The Reward card offer is part of the credit card group, so it is suppressed for Paul for 30 days. The second contact policy that suppresses the Reward card offer for 7 days if it is rejected twice in any channel in the last 7 days does not apply because Paul did not reject the Reward card offer twice in any channel in the last 7 days. Verified References: [Certified Pega Decisioning Consultant | Pega Academy], Suppression policies system Following is the description of the image that was sent with question no:5:

• This is a screenshot of a table with four columns and two rows.
• The table has a header row with white text on a blue background.
• The header row reads “Constraint name”, “Constraint mode”, “Constraint value”, and “Channel”.
• The second row has black text on a white background.
• The second row reads “Standard card”, “Return any action that does not exceed”, “100”, and “Daily”.
• The table has a gray border and a light blue background.