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| Vendor: | Pegasystems |
|---|---|
| Exam Code: | PEGAPCDC87V1 |
| Exam Name: | Certified Pega Decisioning Consultant (PCDC) 87V1 |
| Exam Questions: | 184 |
| Last Updated: | February 28, 2026 |
| Related Certifications: | Pega Certified Decisioning Consultant |
| Exam Tags: |
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Reference module: Creating and understanding decision strategies.
What does a dotted line from a Group By component to a Filter component mean?
Introduction to Group By and Filter Components:
The Group By component groups data based on specified properties, allowing aggregation of data points.
The Filter component is used to include or exclude actions based on specific criteria.
Dotted Line Relationship:
In Pega's visual strategy canvas, a dotted line between two components indicates a reference relationship rather than a direct data flow.
When a dotted line connects a Group By component to a Filter component, it signifies that the Filter component is using a property defined or aggregated in the Group By component.
Functionality Explanation:
For example, if the Group By component aggregates customer data by region, the Filter component can then use this aggregated data to filter actions for specific regions.
This relationship ensures that the filtering process considers the aggregated data, making the decision strategy more dynamic and contextually aware.
Verification from Pega Documentation:
As outlined in Pega's documentation, a dotted line signifies that the Filter component references a property from the Group By component, enabling more complex and accurate decision-making processes.
A bank is currently displaying a group of mortgage offers to its customers on their website. The bank wants to suppress the mortgage group for 1 month if a customer ignores three mortgage offers within that group. How do you define the suppression rule for this requirement?
Context: The bank wants to suppress mortgage offers if a customer ignores three offers within a group for one month.
Suppression Rule: In Pega, suppression rules are defined to manage overexposure to customers.
Implementation: The suppression rule should be set at the group level to ensure the entire group of mortgage actions is suppressed for 30 days after three rejections.
Pega Configuration: The configuration for suppression rules supports this requirement by allowing the setting of a threshold for rejections across any channel, thus making option A the correct choice.
A strategy designer has created 10 actions in the Sales/Credit Cards group and 10 actions in the Sales/Mortgages group. He would like to import all 10 actions from the Credit Cards group and only two actions from the Mortgage group into one decision strategy. What is the minimum number of Proposition Data components he needs to use in his strategy?
Proposition Data Components - These components in a decision strategy are used to import and reference actions or propositions.
Requirement - The strategy designer wants to import all actions from one group and a subset from another.
Minimum Number Calculation:
One component for importing all 10 actions from the Sales/Credit Cards group.
Another component for importing the 2 specific actions from the Sales/Mortgages group.
Pega Customer Decision Hub User Guide 8.6, Section on configuring and using Proposition Data components in strategies .
An outbound run identifies 100 Standard Card offers, 50 on email and 50 on the SMS channel. If the above volume constraint is applied, how many actions will be delivered by the outbound run?

With the volume constraints applied (75 maximum daily actions for StandardCard, 50 maximum daily actions for Email, and 50 maximum daily actions for SMS), the outbound run will deliver a total of 75 actions. This includes actions across different channels while adhering to the specified constraints.
Setting and applying volume constraints in outbound strategies (Page 45-46)
Understanding outbound channel processing and constraints (Page 29-30)
A bank has been running traditional marketing campaigns for many years. One such campaign sends an offer email to qualified customers on day 1. On day 3, it sends a reminder email to customers who haven't responded to the first email. On day 7, it sends a second reminder to customers who haven't responded to the first two emails. If you were to re-implement this requirement using the always-on outbound customer engagement paradigm, how would you approach this scenario?
To re-implement the requirement using the always-on outbound customer engagement paradigm, configure the primary schedule to run daily and let the AI choose the best action from all the actions that a customer qualifies for based on engagement policies. This approach leverages AI to dynamically select the most relevant next best actions, ensuring timely and personalized customer interactions.
Configuring next-best-action settings and engagement policies (Page 34-36)
Using AI to prioritize actions based on customer relevance and business priority (Page 37-38)
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