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U+ Bank, a retail bank, follows all engagement policy best practices to present credit card offers on their website. The bank has introduced a new credit card offer, the Rewards card. Anna, an existing customer, currently holds a higher value card. Premier Rewards, and does not see the new Rewards card offer.
What condition possibly prevents Anna from seeing the new Rewards card offer?
The condition that possibly prevents Anna from seeing the new Rewards card offer is 'Eligibility.' Since Anna already holds a higher value card, Premier Rewards, the eligibility criteria might exclude her from receiving offers for the Rewards card. This ensures that customers are not offered products that are less relevant or valuable compared to what they already have.
In Pega Customer Decision Hub, the characteristics of an action are defined by using
In Pega Customer Decision Hub, the characteristics of an action are defined by using properties. Properties are used to capture specific details about an action, such as the type of offer, the conditions under which it should be presented, and other relevant data that influence decision-making. These properties ensure that the actions are personalized and relevant to the customer.
U + Bank, a retail bank, has applied business weight to their credit card offers to manually nudge the offers. The bank analyzes the effect of the change in Scenario Planner. The following image shows the projected reach and responses of the cards in the comparison mode. How many customers are likely to accept the Standard card?
Understanding the Scenario Planner Output:
The image shows the projected reach and projected responses for several credit card offers, including the Standard card.
The 'Projected responses' column indicates the number of customers expected to respond positively to each offer.
Interpreting the Data for the Standard Card:
For the Standard card, the 'Projected responses' is 21, with a change indicator showing a decrease of 7.
This decrease is factored into the projected response, meaning the expected number of positive responses after considering the applied business weight.
Step-by-Step Calculation:
The base projected responses for the Standard card is 21.
The decrease of 7 has already been factored into this projection.
Therefore, the number of customers likely to accept the Standard card remains 21, as it represents the final projected response after adjustments.
Verification from Pega Documentation:
The Scenario Planner in Pega Customer Decision Hub provides these projections to help visualize the impact of business weight adjustments and other lever changes on customer actions.
U+ Bank recently introduced a new credit card offer, Platinum Plus, for its premium customers. As the bank has some financial targets to meet, the business has decided to boost the Platinum plus card.
As a decisioning consultant, how can you ensure that the Platinum Plus offer is prioritized over other offers?
To ensure that the Platinum Plus offer is prioritized over other offers, you can increase the 'business weight' of the Platinum Plus card. The business weight is a lever used in the arbitration process to influence the prioritization of actions. By increasing the business weight, you make the Platinum Plus offer more likely to be selected over other competing offers.
Reference module: Creating and understanding decision strategies
In a Prioritize component, the top action can be determined based on the value of ________ .
In a Prioritize component in Pega Customer Decision Hub, the top action is determined based on the propensity value. Propensity refers to the likelihood that a customer will respond positively to a given action. Here's a detailed explanation:
Definition of Propensity:
Propensity is a score that indicates the probability of a customer taking a desired action, such as accepting an offer or responding to a campaign. It is calculated using predictive models that analyze historical data and customer behavior.
Role in Prioritize Component:
The Prioritize component in a decision strategy uses the propensity values to rank actions. Actions with higher propensity values are given higher priority, ensuring that the most relevant and likely-to-succeed actions are selected for the customer.
Configuration Steps:
Step 1: In the strategy canvas, add a Prioritize component.
Step 2: Configure the component to use the propensity value for ranking actions. This can be done by selecting the appropriate property that holds the propensity score, typically provided by a prediction model.
Step 3: Connect the Prioritize component to other components in the strategy to ensure the highest priority actions are selected and offered to the customer.
Benefits:
Using propensity to determine the top action helps in making data-driven decisions, increasing the chances of customer engagement and improving overall campaign effectiveness.
Pega-Customer-Decision-Hub-User-Guide-85.pdf: 'Understanding the Next-Best-Action strategy framework' section.
Pega documentation on 'Creating and understanding decision strategies'.
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