Free PEGACPDC88V1 sample questions

Correct answers: Two separate contact policies, one for the Email channel and one for the SMS channel, with their respective timeframes and limits.; A suppression rule applied to the Email channel that is triggered by the acceptance of an action in the SMS channel, with a 48-hour suppression period.

This is necessary to handle the distinct limits for each channel (3 emails in 7 days, 1 SMS in 24 hours).

This is required to implement the cross-channel dependency (no email for 48 hours after an SMS).

Correct answer: Create a new, separate revision for the urgent APR change. Mark it as a high-priority change, complete an expedited peer review, and deploy it independently of the other revision. Then, ensure the change is merged into the standard revision.

This is the best practice for handling urgent or emergency changes. Revision Manager allows for multiple revisions to be worked on in parallel. Creating a separate, dedicated revision for the urgent change isolates it from the larger, less urgent campaign work. This allows the critical regulatory change to be tested and deployed rapidly without pulling in unfinished and untested features from the other revision, minimizing risk. After deployment, the change can be incorporated into the main revision to ensure consistency.

Correct answer: True

This is true. An Action represents the core offer or proposition (e.g., '15% Off Home Insurance'). Treatments are the specific ways that Action is rendered in different channels. You would define a Web treatment (e.g., a banner image) and an Email treatment (e.g., HTML content) and associate both with the same Action.

Correct answer: Suitability

Suitability rules are used to determine if an action is appropriate for the customer based on their individual circumstances, often related to risk, affordability, or ethical considerations. A high debt-to-income ratio is a classic example of a suitability condition designed to ensure the bank is offering products responsibly. Eligibility (e.g., age) and Applicability (e.g., not having the product already) were both met.

Correct answer: The maximum number of actions to return should be set to a lower value (e.g., 1 or 2) for the mobile container.

Real-time containers are configured with a 'Max number of actions' setting. For a web page with a large banner area or carousel, you might return 3-5 offers. However, for a mobile app where screen real estate is at a premium, it is a best practice to configure the container to return only one or two of the absolute best actions to avoid cluttering the user interface.

Correct answer: adjust action priority based on real-time situational relevance

Context Weighting ('C') allows the system to dynamically increase or decrease the priority of an action based on the current context of the interaction. For example, if a customer is on the 'Billing' page of a website, a 'Paperless Billing' offer's context weight can be increased, making it more relevant and likely to be shown at that specific moment.

Correct answers: Proposition Data; Data Join; Decision Table

The Proposition Data component is required to import the available welcome package actions ('Standard', 'Silver', 'Gold') into the strategy.

A Data Join component is needed to query the external database table and enrich the strategy with the customer's loyalty status.

A Decision Table is the ideal component to implement the business logic that maps combinations of policy type and loyalty status to the correct welcome package.

Correct answer: To organize actions into a logical business taxonomy for management, arbitration, and reporting.

The Issue/Group hierarchy provides the fundamental structure, or taxonomy, for all actions. It allows the business to organize actions logically (e.g., Issue: 'Acquisition', Group: 'Credit Cards'). This structure is then used by Next-Best-Action Designer to control which actions are considered, how engagement policies are applied, and how results are reported.

Correct answer: Configure a real-time event stream (e.g., Kafka) that captures return events and writes them to a dedicated 'Recent Returns' data set, and update the suppression rule to check this data set.

Relying on a daily batch process creates a window of time where the customer data is stale. The best practice for handling near real-time suppression is to capture the business event (the return) as it happens and stream it into Pega. A data flow can listen to this stream and update a dedicated data set (like a Decision Data Store) that the suppression logic can check during a real-time decision, ensuring immediate suppression.