PEGACPDC25V1 Certified Pega Decisioning Consultant 25 Question and Answers

When you create a new trigger in the Next-Best-Action Designer, Pega Customer Decision Hub automatically generates a decision strategy for that trigger and channel. You do not need to create or modify any strategies manually. Verified References: Pega Academy - Decisioning Consultant - Creating triggers

According to the best practice, a single change request should be created in the 1:1 Operations Manager portal, because both changes are related to the engagement policies and propositions that are managed in this portal. The Pega Customer Decision Hub portal is used to design and test the decision strategies, not the engagement policies and propositions. Verified References: Pega Decisioning Consultant | Pega Academy

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To update the cashback offer, you need to edit the engagement policy and the action details. Editing the engagement policy allows you to add a new channel (SMS) and update the suitability condition (lowering the debt-to-income ratio). Editing the action details allows you to specify the treatment for each channel (web and SMS). Verified References: [Pega Decisioning Consultant | Pega Academy]

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

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

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Next-Best-Action is a type of decision that involves selecting and prioritizing the most appropriate proposition for each customer at any given moment. Next-Best-Action can be applied to decisions that require customer-centricity, personalization, and contextualization. Determining if a borrower gets a loan is an example of such a decision, as it depends on the customer’s attributes, behaviors, preferences, and needs. The other options are examples of decisions that are not related to customer interactions, but rather to operational or analytical processes. Verified References: [Pega Decisioning Consultant | Pega Academy]

The business weight is a parameter that allows you to manually adjust the priority of an offer based on your business objectives and preferences. The business weight is multiplied by the business value and the propensity to calculate the final priority score of an offer. A higher business weight means a higher priority score, making the offer more likely to be selected and presented to the customer. Therefore, if you want to boost an offer, you can increase its business weight. 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]

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 the requirement that customers do not receive more than four promotional emails per quarter, regardless of past responses to that action by the customer. You can configure the volume constraint to limit the number of actions per channel per quarter and select the option to ignore previous responses. Outbound channel limits are used to limit the number of customers contacted per channel per run, not per quarter. Suppression policies are used to exclude customers from receiving an action based on certain conditions, such as opt-out preferences or recent purchases, not based on the number of times the action is presented. Suitability rules are used to determine whether an action is suitable for a customer based on their propensity, priority, or other criteria, not based on the number of times the action is presented. 

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]

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 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. 

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

To achieve this requirement, you need to ensure that two tasks are complete in the Channel tab of the Next-Best-Action Designer: enable a web channel and map the real-time container to a business structure level. A web channel is a type of channel that allows you to deliver actions and treatments to your customers through your website or web app. You need to enable a web channel and configure its settings, such as authentication, security, and response format. A real-time container is a configuration that defines how to deliver actions and treatments to a specific channel. You need to map the real-time container that contains the Reward Card offer to a business structure level, such as Top-level or Issue-level, so that it can be available for delivery through the web channel. Verified References: [Pega Academy - Decisioning Consultant - Configuring channels], [Pega Academy - Decisioning Consultant - Configuring real-time containers]

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

To implement this requirement, you need to create an action and the associated web treatment, a real-time container, and a business structure. An action is a proposition that you want to present to a customer, such as a credit card offer. A treatment is the way you present the action to a customer, such as an image or a text message. A real-time container is a configuration that defines how to deliver actions and treatments to a specific channel, such as a website or a mobile app. A business structure is a hierarchy of business groups and business issues that organizes actions into meaningful categories. Verified References: Pega Academy - Decisioning Consultant - Creating actions and treatments, Pega Academy - Decisioning Consultant - Configuring real-time containers, [Pega Academy - Decisioning Consultant - Defining business structure]