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Pegasystems PEGACPDC25V1 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Contact policy and volume constraints: This domain covers managing contact policies to limit how often customers receive communications, focusing on controlling action exposure and volume in outbound channels while configuring appropriate tracking periods.
Topic 2
  • Channels: Covers implementing real-time containers, offer emails, and third-party integrations for multi-channel delivery.
Topic 3
  • Engagement policies: Addresses creating engagement policies, strategies, and customer journeys that govern contact timing and frequency.
Topic 4
  • AI and Arbitration: Explores AI-powered action arbitration, prioritization, and using business levers for action selection.
Topic 5
  • Next-Best-Action concepts: Covers one-to-one engagement fundamentals, contact center optimization, always-on outbound strategies, and next-best-action optimization.
Topic 6
  • Decision strategies: Focuses on building decision strategies with business logic to power the decisioning engine.
Topic 7
  • Actions and treatments: Focuses on defining and managing customer actions for web and outbound channels.

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Pegasystems Certified Pega Decisioning Consultant 25 Sample Questions (Q63-Q68):

NEW QUESTION # 63
What is the name of the property that the system computes automatically when you use an Adaptive Model decision component?

Answer: A

Explanation:
Propensity is the name of the property that the system computes automatically when you use an Adaptive Model decision component. Propensity is a measure of how likely a customer is to accept an action, based on their past behavior and profile. An Adaptive Model component uses machine learning to calculate the propensity for each action and store it in a property with the same name as the action. Verified References: Pega Academy - Decisioning Consultant - Using adaptive models


NEW QUESTION # 64
You are a decisioning architect responsible for configuring offer prioritization for home loan offers based on the business requirements. Select each prioritization factor on the left and drag it to the correct condition on the right.

Answer:

Explanation:
A close-up of a document AI-generated content may be incorrect.


NEW QUESTION # 65
MyCo, a telecom company, uses Pega Customer Decision Hub to present offers to qualified customers. The business recently decided to send offer messages through the email channel. The Design department has designed an email treatment which includes dynamic placeholders.
As a deaccessioning architect, what do you use in order to test the visualization and the rendering of the email content, including replacing of the placeholders with customer information?

Answer: B

Explanation:
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 Reference: [Pega Academy - Decisioning Consultant - Testing email treatments]


NEW QUESTION # 66
U+- Bank, a retail bank, has recently Implemented a project in which qualified customers see mortgage offers when they log in to the web self-service portal.
Currently, only the customers who satisfy the following engagement policy conditions receive the Fifteen-year fixed-rate mortgage offer:

The bank decides to make two changes:
1. Update the suitability condition for the Fifteen-year fixed-rate mortgage offer.
2. Introduce a new offer, Twenty-year fixed-rate mortgage.
The following table shows the new engagement policy conditions for both mortgage offers:

What is the best practice to fulfill this change management requirement in the business operations environment?

Answer: B


NEW QUESTION # 67

U+ Bank wants to offer credit cards only to low-risk customers. The customers are divided into various risk segments from Good to Very Poor. The risk segmentation rules that the business provides use the Average Balance and the customer Credit Score.
As a decisioning architect, you decide to use a decision table and a decision strategy to accomplish this requirement in Pega Customer Decision Hub.
Using the decision table, which label is returned for a customer with a credit score of 240 and an average balance 35000?

Answer: A

Explanation:
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.


NEW QUESTION # 68
......

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