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Pegasystems PEGACPDC25V1 Exam Syllabus Topics:

SectionWeightObjectives
Next-Best-Action concepts12%- One-to-one customer engagement
  • 1. Optimize the next-best-action strategy
  • 2. Define the starting population
  • 3. Customer engagement blueprint
  • 4. Optimize the customer value in the contact center
  • 5. Essentials of always-on outbound
Decision strategies14%- Strategy design
  • 1. Test decision logic
  • 2. Build decision strategies
  • 3. Use predictive analytics
AI and arbitration12%- Decision optimization
  • 1. Prioritize actions
  • 2. Use AI in decisioning
  • 3. Apply arbitration logic
Engagement policies15%- Customer engagement rules
  • 1. Define customer engagement policies
  • 2. Customer journeys
  • 3. Create an engagement strategy
Business agility in one-to-one customer engagement12%- Agile customer engagement
  • 1. Support business-driven changes
  • 2. Adapt engagement strategies quickly
  • 3. Optimize customer experiences
Contact policy and volume constraints13%- Contact governance
  • 1. Apply volume constraints
  • 2. Configure contact policies
  • 3. Control customer contact frequency
Actions and treatments12%- Manage customer actions
  • 1. Present a single offer on the web
  • 2. Define and manage customer actions
  • 3. Define an action for outbound
Channels10%- Multi-channel engagement
  • 1. Web channel configuration
  • 2. Cross-channel engagement
  • 3. Outbound channel setup

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Pegasystems Certified Pega Decisioning Consultant 25 認定 PEGACPDC25V1 試験問題 (Q29-Q34):

質問 # 29
U+ Bank, a retail bank, presents offers on its website by using Pega Customer Decision Hub. The bank wants to leverage Customer Decision Hub capabilities to present relevant offers to qualified customers. As a decisioning consultant, you are responsible for configuring the business requirements with the Next-Best-Action Designer, which involves several tasks. To accomplish these tasks, you might have to use auto-generated decision strategies, create new decision strategies, or edit existing strategies.
In the Answer Area, select the correct execution for each Task.

正解:

解説:


質問 # 30
A declsioning architect wants to use the customer properties Gender and MonthlyAverageUsage in a Data Join component. Which decision component is required to enable access to these properties?

正解:C


質問 # 31
U+ Bank, a retail bank, is facing an unforeseen technical issue with its customer care system. As a result, the bank wants to share the new temporary contact details with all customers over an SMS. All customers must receive this communication regardless of the engagement policy conditions and constraints.
Which type of communication do you configure to implement this requirement?

正解:A


質問 # 32
In a decision strategy, to use a customer property in an expression, you

正解:C

解説:
In a decision strategy, to use a customer property in an expression, you need to define Customer page in Pages & Classes and specify its class as Data-Customer. This allows you to access customer properties by using dot notation, such as Customer.Age or Customer.Gender. You do not need to define the property as a strategy property, use it without any prefix, or prefix it with the keyword Customer. Verified Reference: [Certified Pega Decisioning Consultant | Pega Academy], Decision strategies


質問 # 33
U+ Bank presents various credit card offers to its customers on its website. The bank uses AI to prioritize the offers according to customer behavior. With the introduction of the Gold credit card offer, the offer click-through propensity decreased to 0.42.
What does the decrease in the propensity value most likely indicate?

正解:A

解説:
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 Reference: Pega Decisioning Consultant | Pega Academy


質問 # 34
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