PEGAPCDC87V1 Exam Dumps - Certified Pega Decisioning Consultant (PCDC) 87V1

Updating Scorecard in Decision Strategy: When a scorecard rule is updated to include a new property such as customer income, it is essential to reflect this change in the decision strategy. The scorecard properties must be correctly mapped to ensure the new calculations are utilized.

Scorecard Component - This component in Pega CDH is designed to evaluate attributes and produce a score based on those attributes. It is commonly used for calculating credit scores, risk scores, etc.

Steps to Use Scorecard:

In the decision strategy, add a Scorecard component.

Configure the scorecard to include relevant customer attributes (e.g., payment history, credit utilization).

Set the rules and weights for each attribute to calculate the final credit score.

Use the score in further decision logic (e.g., filtering out customers with a score below 500).

Pega Customer Decision Hub can be applied to various types of decisions, including determining how to retain a customer. It leverages decision management and AI technologies to analyze customer data, predict behaviors, and recommend personalized actions that can help improve customer retention and satisfaction.

Understanding Aggregation Components: Aggregation components in Pega decision strategies are used to perform calculations based on a list of actions. This can include summing values, calculating averages, or other statistical measures.

Use Case of Aggregation Components:

Set a Text Value: This is typically handled by Set Property or Data Transform components.

Filter Actions: Filters are used for filtering actions based on priority and relevance.

Choose Between Actions: This is typically handled by a Decision or Filter component.

Aggregation is specifically used to perform calculations based on a group of actions. It can be used to sum the total value of actions, calculate the average propensity, etc.

Implementation in Decision Strategy:

Add Aggregation Component: In the decision strategy, add an Aggregation component.

Define Calculation: Specify the type of calculation (sum, average, count, etc.) and the actions to be included in the calculation.

The Pega Customer Decision Hub User Guide mentions the use of aggregation components to perform calculations on action lists, confirming their primary function is for making calculations based on a list of actions (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Using aggregation components in decision strategies").

Conclusion: In a decision strategy, aggregation components are used to make calculations based upon a list of actions, enabling the strategy to derive meaningful metrics from groups of actions.

Aggregation components in Pega Customer Decision Hub provide the ability to make calculations based upon a list of actions. These components are used to perform operations such as summing values, averaging scores, or other statistical calculations that aggregate data from multiple actions. This is crucial for generating insights and making informed decisions based on the collective data of several actions.

To ensure that there is no gender bias when presenting mobile data packages for students, you should configure the ethical bias policy to allow a 1 Rate Ratio. This setting ensures that the system will show a warning if there is any deviation from an equal distribution between genders, thereby preventing any gender-based discrimination in offer presentation.

ï‚· 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.

ï‚· 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.

ï‚· 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.

ï‚· 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.

Decision Table Processing: The decision table is configured to evaluate the customer's credit score and average balance to determine the risk segmentation.

Conditions Evaluation:

For a credit score of 240 and an average balance of 35,000:

The first condition checks if the credit score is >= 400 and < 600, and average balance is >= 30,000, which does not match.

The second condition checks if the credit score is >= 200 and < 400, and average balance is >= 20,000, which matches both the credit score (240) and the average balance (35,000).

Therefore, the correct result for these conditions is "Fair".

References: This is derived from typical Pega Decision Table configuration and logical evaluation as described in Pega documentation on using decision tables for such scenarios .