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| Certification Vendor: | Pegasystems |
|---|---|
| Exam Name: | Certified Pega Decisioning Consultant 25 |
| Exam Number: | PEGACPDC25V1 |
| Exam Format: | Scenario-based, Multiple choice, Drag-and-drop |
| Related Certifications: | Pega Certified System Architect (PCSA) Pega Certified Senior Decisioning Consultant |
| Available Languages: | English |
| Exam Price: | $190 USD |
| Real Exam Qty: | 60 |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Recommended Training: | Pega Academy Decisioning Consultant Learning Path |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Pegasystems PEGACPDC25V1 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended: knowledge of Pega Platform, Customer Decision Hub, completion of Decisioning Consultant learning mission |
| Official Syllabus URL: | https://academy.pega.com/exam/certified-pega-decisioning-consultant-5 |
>> Exam Dumps PEGACPDC25V1 Pdf <<
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NEW QUESTION # 51
U+ Bank, a retail bank, has recently implemented a project in which credit card offers are presented to qualified customers when they log in to the web self-service portal. The bank added engagement policy conditions to show the offers based on the bank's requirements.
In the Answer Area, select the correct engagement policy for each condition.
Answer:
Explanation:
NEW QUESTION # 52
What does a dotted line from a "Group By" component to a "Filter" component mean?
Answer: C
NEW QUESTION # 53 
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: B
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 # 54
U+ Bank has recently defined two contact policies:
1. Suppress 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.
2. Suppress the Reward card offer, part of the credit card group, for 7 days if it is rejected twice in any channel in the last 7 days. Paul, an existing U+ Bank customer, no longer sees the Reward card offer. What is the reason that Paul cannot see the offer?
Answer: C
Explanation:
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.
NEW QUESTION # 55
A bank has been running traditional marketing campaigns for many years. One such campaign sends an offer email to qualified customers on day one. On day five, the bank presents a similar offer if the first email is ignored.
If you re-implement this requirement by using the always-on outbound customer engagement paradigm, how do you approach this scenario?
Answer: B
Explanation:
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.
NEW QUESTION # 56
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