PEGAPCDC87V1 Practice Exam Questions with Answers Certified Pega Decisioning Consultant (PCDC) 87V1 Certification

To update the cashback offer to be presented through a new channel (SMS) and to update the suitability condition, you need to perform two main tasks: adding a new treatment and editing the engagement policy.

Add a New Treatment:

Step 1: Navigate to the "Actions" section in the Pega Customer Decision Hub.

Step 2: Locate the existing cashback offer action.

Step 3: Add a new treatment specifically for the SMS channel. This involves creating a new SMS treatment that defines the message content, format, and any other relevant details.

Step 4: Ensure the new treatment is linked to the cashback offer action.

Edit the Engagement Policy:

Step 1: In the Pega Customer Decision Hub, go to the "Engagement Policies" section.

Step 2: Locate the engagement policy associated with the cashback offer.

Step 3: Update the suitability condition by lowering the debt-to-income ratio threshold from 48 to 45. This ensures that the new condition is applied when evaluating customer eligibility for the cashback offer.

Step 4: Save and publish the updated engagement policy to ensure it takes effect.

Verify the Changes:

Step 1: Test the new SMS treatment by sending test messages to a seed list to verify the content and formatting.

Step 2: Ensure that the updated engagement policy correctly filters customers based on the new debt-to-income ratio threshold.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Defining treatments for actions" and "Configuring engagement policies" sections.

Pega documentation on "Creating and managing actions" and "Editing engagement policies".

Business Requirement: The need is to implement risk segmentation rules based on age and credit score.

Decision Table Integration: The decision table can be added to the decision strategy where it will evaluate the credit score and age, and then assign the appropriate risk segment.

Parameter Passing: The decision table within the strategy will use the credit score passed to it as a parameter to determine the segmentation.

References: Pega's methodology for incorporating decision tables into strategies for evaluating and segmenting customers based on specific criteria is well documented in the user guides .

? Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

? Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

Set-Property Component: This component performs the calculation of profit for each of the four brands (Nike, Adidas, Puma, Reebok). Since the Set-Property component is configured to calculate profit for each brand individually, it processes four inputs, resulting in four outputs.

To present offers from both groups and arbitrate across the two groups to select the best offer based on customer behavior, you need to map a real-time container to the Top-level or Issue-level in the Next-Best-Action Designer. This mapping allows the system to evaluate and arbitrate offers from different groups based on the defined criteria and customer behavior, ensuring the most relevant offers are presented.

Next-Best-Action Strategy Application: Pega's Next-Best-Action strategy can be applied to various business issues to optimize decision-making and enhance customer interactions.

Collections: Used to manage and optimize debt collection processes by determining the best actions to take with customers who have outstanding debts.

Service: Enhances customer service interactions by suggesting the most relevant actions or responses to customer inquiries and issues.

Retention: Focuses on retaining customers by identifying and executing actions that are most likely to keep them engaged and loyal.

? Marketing and Sales Actions: Creating actions like "No annual fee credit card" and "1% cash back credit card" address different customer needs and stages in the customer lifecycle, including acquisition and engagement.

? Full Customer Lifecycle: These actions involve marketing and sales efforts aimed at attracting and retaining customers by offering attractive benefits.

In designing the next-best-action prioritization expression that balances customer needs with business objectives, you need to consider "Predicted customer behavior." This factor involves using predictive models to forecast how customers are likely to respond to different actions, ensuring that the recommended actions are both relevant to the customer and aligned with business goals.

Identify the Requirement: U+ Bank wants to offer credit cards only to customers with specific risk segments and also ensure that the age of the customer is greater than 25 years. This involves setting eligibility criteria for credit card offers.

Understand Engagement Policies: Engagement policies in Pega CDH are used to define rules that govern when specific actions are relevant for a customer. This includes eligibility, applicability, and suitability conditions.

Access Next-Best-Action Designer: To implement these criteria, navigate to the Next-Best-Action Designer within Pega Customer Decision Hub.

Engagement Policy Tab:

Eligibility Criteria: Set the criteria to include customers with an age greater than 25 years.

Define the other conditions based on Credit Score and Average Balance to segment customers into Good, Fair, Poor, and Very Poor risk categories.

According to the Pega Customer Decision Hub User Guide, the engagement policy tab allows users to define when specific actions or groups of actions are appropriate for customers (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer engagement policies").

Implementing the Criteria:

Navigate to the Engagement Policy tab: This is within the Next-Best-Action Designer.

Add a New Policy: Create a new engagement policy where you define the eligibility criteria for the credit card offer.

Set Age Condition: Ensure the age condition (Age > 25) is part of the eligibility criteria.

Validation:

Ensure the conditions are tested thoroughly to verify that the credit card offers are only presented to customers meeting the set criteria.

Additional Resources:

For detailed steps on configuring engagement policies, refer to the Pega documentation on "Configuring engagement policies for actions" (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf).

Conclusion: The best practice to ensure that credit cards are offered only to customers with age greater than 25 is to use the Engagement Policy tab in the Next-Best-Action Designer, where you can set the eligibility conditions appropriately.

Correct Action Hierarchy Levels:

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Action Hierarchy Level: Action

Retention

Action Hierarchy Level: Business Issue

Tablets

Action Hierarchy Level: Group

Service

Action Hierarchy Level: Business Issue

Change address

Action Hierarchy Level: Action

Proactive retention

Action Hierarchy Level: Group

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Action: This is a specific offering or initiative that can be presented to customers. It is a specific action within the broader strategy.

? Requirement:

U+ Bank has added five new actions to the Credit Cards group and wants to enable these actions in the email channel.

? Configuring Each Action with an Email Treatment:

Each action must be configured with an appropriate treatment for the email channel.

Treatments define how the actions are presented in the channel, including the content and format of the email.

This step ensures that the actions are prepared to be sent via email with the correct content.

To temporarily suspend sending outbound messages and instead write the selected customers and action details to a database table for later offline processing, the most efficient way is to update the Send shape with Finalization in all the action flows. This approach allows the system to complete the processing of actions without sending the messages, and instead, store the necessary details in a database for later use.

To use a customer property in an expression within a decision strategy in Pega, follow these steps:

Identify the Customer Property:

Determine the specific customer property you want to use in your strategy.

Prefix with 'Customer':

In Pega, customer properties are accessed by prefixing them with the keyword Customer. This ensures that the property is correctly referenced from the customer data.

Use the Property in Expression:

When defining an expression in a decision strategy, include the Customer prefix. For example, if the property is Age, use Customer.Age.

Verify Property Definition:

Ensure that the property is correctly defined in the data model and accessible within the strategy context.

References:

Pega Customer Decision Hub User Guide 8.6: Understanding the Context Dictionary, Configuring the Context Dictionary??.

To present offers based on the responses of a test group that already received the offer, you should configure the propensity factor in Pega Customer Decision Hub. Propensity models predict the likelihood of a customer accepting an offer based on historical data and responses from similar customers. By leveraging propensity, you can ensure that offers are presented to customers who are most likely to respond positively, based on the behavior of the test group.

To determine which of the new credit card offers (Diamond, Platinum, and Gold) will be the best performing action, the Action Distribution Report is used. This report helps in running a distribution test to analyze how the different offers are being distributed among the customer base, providing insights into which offer might perform the best based on initial distribution and eligibility criteria.

A solid arrow from a "Set Property" component to a "Filter" component in a Pega decision strategy indicates that a property set by the "Set Property" component is being referenced or used by the "Filter" component. This relationship ensures that the data set in the "Set Property" component influences the filtering criteria in the "Filter" component, allowing for more dynamic and contextually relevant decisions.

With the volume constraints applied (75 maximum daily actions for StandardCard, 50 maximum daily actions for Email, and 50 maximum daily actions for SMS), the outbound run will deliver a total of 75 actions. This includes actions across different channels while adhering to the specified constraints.

To access a property from an unconnected component in Pega Customer Decision Hub, you use the component-dot-property construct. This allows you to reference properties from other components within your strategy by specifying the component name followed by the property name. This construct helps in accessing and using data across different components without directly connecting them.

To prevent offer overexposure, the appropriate suppression rule is to suppress a group of actions for 10 days if there are three clicks on the web channel in the last 10 days. This ensures that once a customer interacts with any mortgage offer three times within a 10-day period, they will not see any mortgage offers for the next 10 days. This rule helps in maintaining a good customer experience by avoiding overexposure.

To present a free 2GB data offer when customers contact the call center for mobile number portability, you should configure context weighting in Pega Customer Decision Hub. Context weighting allows you to prioritize actions based on the specific context of the interaction, such as the reason for the call. By setting a higher weight for the free data offer in the context of mobile number portability, you can ensure this offer is prioritized when relevant.

To determine the label returned by a decision table for a customer with a credit score of 115 and an average balance of 15000, you need to examine the decision table rules. Based on the typical structure of decision tables, a credit score of 115 and an average balance of 15000 would fall within the ranges specified for the label 'B'.

Referencing Customer Properties: In Pega decision strategies, customer properties are accessed by prefixing the property name with "Customer." This convention helps in clearly identifying the context of the properties being used.

Correct Engagement Policy Configuration:

Age

Engagement policy: Group-level eligibility

Opt-out

Engagement policy: Group-level applicability

Debt-to-income ratio

Engagement policy: Action-level suitability

Age

Group-level eligibility: This criterion determines whether the customer is eligible to receive any credit card offers. Eligibility conditions ensure that only customers meeting the basic criteria (e.g., being above the age of 18) are considered for the offers. This condition applies to all credit card offers within the group.

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.

Correct Order of Implementation:

Create a change request

Define action details and engagement policies

Rank the change request

Perform persona testing

Approve the change request

Complete and deploy the revision

Step by Step Comprehensive Detailed Explanation with References:

Create a change request:

The first step is to create a change request within the Business Operations Environment. This involves initiating a request for the new offer and specifying what needs to be changed or added.

? Arbitration Factors: To prioritize customer needs in the decision strategy, adjusting the context weighting factor is essential. This factor takes into account the situational context of each action, thereby prioritizing actions that align with customer needs.

? Implementation: By boosting context weighting, you ensure that the decision strategy considers the specific context of customer interactions, leading to more relevant and satisfactory offer presentations.

To configure the arbitration factor for nudging the Platinum Plus credit card to customers who visit the home page, you need to focus on action value. Action value takes into account the business value or importance of an action, which aligns with the goal of promoting a specific credit card to customers.

Arbitration in Pega CDH - Arbitration is the process of ranking actions based on their priority. Factors like propensity, context weighting, action value, and business levers are considered during arbitration.

Action Value - This factor assesses the intrinsic value of an action, which includes its business value. It is crucial when a business objective, like promoting a specific credit card, needs to be prioritized.

Implementation - By configuring action value, you ensure that the Platinum Plus credit card offer has a higher priority over other actions for customers visiting the homepage.

To limit the number of emails sent to customers irrespective of past outcomes with a particular offer and customer, customer contact limits should be implemented. Customer contact limits help control the frequency of communications with customers across different channels, ensuring that they do not receive an excessive number of messages.

Business Levers: These are used to apply weights to different actions or groups of actions to influence their prioritization based on business objectives.

Context Weighting: This factor is used to adjust the priority of actions based on the specific context of the customer interaction, ensuring that both business objectives and customer needs are balanced.

References: The configuration and utilization of business levers and context weighting within the Next-Best-Action Designer are key components in aligning the decision strategy with both business and customer goals .

? Understand the strategy objective: The decision strategy is designed to output the most profitable shoe. The profit is calculated as Profit = Price - Cost.

? Evaluate the provided data:

Nike: Profit = $69 - $59 = $10

Adidas: Profit = $65 - $45 = $20

Puma: Profit = $85 - $65 = $20

Reebok: Profit = $75 - $50 = $25

? Determine the most profitable shoe: According to the calculations, Reebok has the highest profit ($25).

? Fill in the blank: Therefore, the output of the component in the blank space highlighted in red should be Reebok, which has the highest profit.

? Reference: The strategy design and output decision rules are outlined in the sections on "Understanding Next-Best-Action Designer arbitration" and "Next-Best-Action strategy framework components" in the Pega Customer Decision Hub User Guide??.

Understanding the Scenario:

There are two segments: Segment A and Segment B.

Segment A references Segment B.

The goal is to ensure that Segment B is automatically refreshed when Segment A is refreshed.

Segment Refresh Settings:

Refreshable Segment: This setting determines if a segment can be refreshed automatically.

Refresh Child Segments: This setting ensures that when a parent segment is refreshed, all its child segments are also refreshed.

Configuration Requirement:

To achieve the automatic refresh of Segment B when Segment A is refreshed, you need to enable both the Refreshable option for Segment B and the Refresh Child Segments option for Segment A.

Implementation:

Segment A: Enable the Refresh Child Segments option.

Segment B: Enable the Refreshable option.

Correct Configuration:

The correct configuration is to set Segment A with Refresh Child Segments enabled and Segment B with Refreshable enabled.

Enrichment decision components in Pega Customer Decision Hub provide the ability to set customer properties. These components are used to enhance the customer data within a decision strategy by adding or updating information about the customer. This allows for more personalized and contextually relevant decisions.

Definition and Purpose:

Enrichment components are used within decision strategies to add or update customer data, ensuring that the decisioning process has the most relevant and up-to-date information.

This can include setting properties like customer preferences, behaviors, or other attributes that might influence the decision outcome.

Types of Enrichment Components:

Set Property: This is the primary enrichment component used to set a value to a property within the strategy.

Data Import: Importing data from external sources to enrich the customer profile.

Data Transform: Applying transformations to data to format or manipulate it as needed within the strategy.

Usage in Decision Strategies:

When designing a decision strategy, enrichment components are typically used in the initial stages to ensure that all necessary customer data is available and accurate before proceeding to decision logic components.

For example, before evaluating eligibility or applicability, you might use an enrichment component to update the customer's credit score or recent transaction history.

Implementation Steps:

Step 1: Drag the enrichment component into the decision strategy canvas.

Step 2: Configure the component by selecting the property to set and defining the value or expression to be used.

Step 3: Connect the enrichment component to other components within the strategy to ensure the enriched data is utilized in subsequent decision-making steps.

Example:

Consider a scenario where a bank wants to offer personalized loan options based on the customer's recent financial activities. An enrichment component can be used to update the customer's profile with their latest transaction data, ensuring that the loan offer is tailored to their current financial status.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section, which describes the use of enrichment components in decision strategies.

Pega documentation on "Creating and understanding decision strategies".

To select the best offer from both groups based on customer behavior, you should ensure that the propensity is enabled in the Arbitration tab. Propensity scores predict the likelihood of a customer accepting a particular offer, and by enabling propensity, you can prioritize offers that have a higher likelihood of acceptance, thus aligning offers with customer behavior.

Interaction History Summary - This rule aggregates data like customer responses, action outcomes, and interactions over a set time period.

Steps to Create the Rule:

In Dev Studio, go to Configure -> Decisioning -> Decisions -> Data Sources -> Interaction History Summaries.

Create a new summary rule.

Set the time period to 20 days.

Define the interaction data to be tracked (e.g., accepts, rejects).

Benefits - Enables precise tracking of customer engagement and responses, facilitating better decision strategies based on historical data.

To ensure that customers do not receive more than four promotional emails per quarter, you can use customer contact limits. This setting allows you to define the maximum number of contacts a customer can receive over a specific period, regardless of their responses to previous actions. By setting this limit, you can prevent overcommunication and enhance customer experience.

? Understanding Volume Constraints:

Volume constraints can be applied to limit the number of actions offered to customers.

Constraints can be set to apply to actions as a group or individually.

? Scenario Analysis:

In the given scenario, a customer qualifies for three actions.

The top-ranked action has not reached its volume limit, but the lower-ranked actions have.

Since the constraints are applied as a group, if the top-ranked action is available, it will be selected.

The lower-ranked actions, having reached their limits, will not be selected.

? Conclusion:

Only the top-ranked action will be selected for the customer in the outbound run.

To address the issue where customers have ignored a particular mortgage offer and the bank wants to offer the action 30% more frequently, we need to configure the appropriate arbitration factor in Pega Customer Decision Hub.

Arbitration Overview: Arbitration in Pega CDH determines how actions are prioritized and selected for presentation to the customer. It involves factors like business value, propensity, and action weighting.

Action Weighting: Action weighting is used to adjust the frequency and priority of specific actions. By increasing the action weighting, you effectively make the action more likely to be selected during the arbitration process.

Implementation:

Navigate to the Next-Best-Action Designer in Pega CDH.

Locate the action for the mortgage offer that needs increased frequency.

Adjust the action weighting to increase by 30%. This adjustment modifies the priority calculation, making the action more likely to be selected.

To prevent the same offer from being shown repeatedly to a customer, the correct suppression rule is to suppress an action for 10 days if there are five impressions for the web channel in the last 14 days. This ensures that after a customer has been presented with the same offer five times within a 14-day period, they will not see that offer for the next 10 days, thereby preventing offer fatigue.

Initial Configuration: U+ Bank has implemented Pega Customer Decision Hub for the contact center and now wants to extend it to the web channel.

Understand Channel Configuration: Pega CDH requires the configuration of channels in the Next-Best-Action Designer to ensure that treatments are correctly recommended.

Modify the Strategy Framework:

Next-Best-Action Framework: This strategy framework is used to determine the best actions for customers across various channels. It needs to be modified to include configurations specific to the web channel.

Steps to Modify:

Access Next-Best-Action Designer: Navigate to the Next-Best-Action Designer.

Channels Tab: Configure the new web channel in the Channels tab.

Modify the Strategy: Update the Next-Best-Action Framework strategy to incorporate the new web channel. This involves ensuring the new actions and treatments are included and prioritized correctly for the web channel.

According to the Pega Customer Decision Hub User Guide, modifying the framework strategy to cater to new channels is necessary for extending customer engagement (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer channels").

Testing and Validation:

Test the new configurations to ensure treatments for the web channel are correctly recommended in the next-best-action suggestions.

Conclusion: To configure the new treatments for the web channel in the next-best-action recommendations, the Next-Best-Action Framework strategy must be modified to cater to the new web channel.

Pega Scenario Planner is the best tool for estimating the business value of a next-best-action run with the selected configuration:

Purpose of Scenario Planner:

Step 1: Pega Scenario Planner is designed to simulate and analyze the potential impact of business changes before they are implemented.

Step 2: It provides insights into how changes in decision strategies, engagement policies, or other configurations affect business outcomes.

Simulation Setup:

Step 1: Define the scenarios you want to test, including changes to the decision strategies or engagement policies.

Step 2: Configure the simulation to reflect the real-world conditions and customer data relevant to the business case.

Analyzing Results:

Step 1: Run the simulation and review the results to understand the estimated business value generated by the next-best-action run.

Step 2: Analyze key performance indicators (KPIs) such as acceptance rates, response rates, and financial metrics to evaluate the impact of the changes.

Example:

U+ Bank can use Pega Scenario Planner to simulate the presentation of Credit Card offers on their self-service portals, allowing them to estimate the business value generated by these offers based on the selected configuration.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Increasing your understanding of the effect of your business changes with the Scenario Planner" section.