Pegasystems PEGAPCDC87V1 Certified Pega Decisioning Consultant (PCDC) 87V1 Exam Practice Test

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 .

In Pega Customer Decision Hub, the Next-Best-Action is re-evaluated in a call-center context when:

The service representative completes a task: Completing a task can change the context or status of the interaction, triggering a re-evaluation of the next best action to ensure the recommendations are still relevant.

A customer responds to an offer: Customer responses provide new information that can influence subsequent recommendations. A positive or negative response to an offer can change the customer's engagement status and preferences.

A customer changes the call context: Changes in the context of the call, such as switching topics or expressing new concerns, can prompt a re-evaluation to provide the most appropriate next actions based on the updated context.

 Action Availability Configuration: To ensure that a specific action is not selected for any customers, you need to set its availability to "Never." This setting ensures that the action will not be available for selection in any campaign or strategy, effectively preventing its use.

 Implementation: Navigate to the action configuration settings and set the availability to "Never." This will ensure that the action is not picked up by the decision strategy or any outbound run.

 Business Structure: Setting up the business structure to Sales/CreditCards ensures that the actions and offers are correctly categorized and managed within the appropriate business context.

 Real-Time Container: Creating and configuring a real-time container is necessary for presenting the credit card offer to customers in real-time as they log in to the website.

 Action and Web Treatment: Defining the action and configuring its web treatment ensures that the offer is ready to be displayed to the customers, complete with the necessary details and presentation format.

To re-implement the bank's traditional marketing campaign using the always-on outbound customer engagement paradigm in Pega, follow these steps:

Understand the Traditional Campaign Requirements:

Day 1: Send an offer email to qualified customers.

Day 3: Send a reminder email to customers who haven't responded to the first email.

Day 7: Send a second reminder to customers who haven't responded to the first two emails.

Set Up Next-Best-Action Designer:

Use the Next-Best-Action Designer to define engagement policies, arbitration, and channels to manage customer interactions.

The always-on approach continuously evaluates customer interactions and selects the best action based on real-time data and AI-driven insights.

Configure the Primary Schedule:

Set up a daily run schedule for the Next-Best-Action strategy. This allows the AI to evaluate customer interactions daily and decide the best action to take.

Leverage AI for Decisioning:

Configure the AI models to determine the best action for each customer based on their interaction history and engagement policies. The AI will automatically handle:

Sending the initial offer email on Day 1.

Sending a reminder email on Day 3 to those who haven't responded.

Sending a second reminder on Day 7 to those who still haven't responded.

Define Engagement Policies:

Eligibility: Define which customers qualify for the offer.

Applicability: Ensure the offer is relevant to the customer.

Suitability: Verify that sending the offer is in the best interest of the customer.

Contact Policies: Set rules to manage the frequency and timing of emails to avoid over-communication.

Implement AI-Driven Actions:

Use the Next-Best-Action strategy framework to select actions (e.g., sending emails) based on the AI model's recommendations.

Configure treatments (email content) and channels (email) within the Next-Best-Action Designer.

Monitor and Adjust:

Continuously monitor the performance of the campaign using Pega's analytics and adjust the strategy as needed to optimize engagement and response rates.

References:

Pega Customer Decision Hub User Guide 8.5: Understanding Next-Best-Action Designer basics, Configuring the Next-Best-Action Designer for Pega Customer Service, Setting constraints contact policy limits and controls​​.

To ensure that the Gold credit card offer is presented only to customers who have an annual income of more than USD 150,000, you need to configure an engagement policy condition in the Next-Best-Action Designer. Here’s why:

Engagement Policy Condition:

Engagement policies in the Next-Best-Action Designer are used to define conditions that determine whether a particular action (such as offering a Gold credit card) is suitable or eligible for a customer.

By setting an engagement policy condition based on the customer's annual income, you ensure that only those customers who meet the specified income threshold are eligible to receive the offer.

This condition acts as a filter that evaluates each customer’s income and includes only those who earn more than USD 150,000.

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.

If the volume constraint shown in the image (Maximum 75 Daily with Channel: Action: StandardCard, Maximum 50 Daily with Channel: Email, Maximum 50 Daily with Channel: SMS) is applied, the outbound run will deliver 75 actions in total. This includes actions across different channels, adhering to the specified limits.

Ensuring Latest Customer Information: To ensure that the outbound run always uses the latest customer information, you need to select the "Refresh the audience" option. This setting ensures that the audience is refreshed with the most recent customer data before the campaign is executed.

Implementation: Navigate to the campaign configuration and select the option to refresh the audience before running the campaign. This guarantees that any changes or updates to the customer data are taken into account.

Next-Best-Action leverages customer lifecycle analysis to anticipate retention issues. By analyzing patterns and trends in customer behavior, Next-Best-Action strategies can proactively address potential retention problems and enhance customer engagement to prevent churn.

Identifying the Requirement:

The bank uses Pega Customer Service in its contact center and wants to use CDH to determine the Next-Best-Action when a call is accepted by a service representative.

The task is to determine what pieces of information are used by Pega CDH to determine the Next-Best-Action recommendations.

Evaluating Pega CDH Capabilities:

Pega CDH integrates seamlessly with Pega Customer Service to leverage real-time customer data and contextual information.

The system uses a combination of call context (information specific to the current call) and customer profile (historical and demographic data) to make informed decisions.

Process Overview:

Call Context:

Includes details such as the reason for the call, current issues or inquiries, and any immediate needs or concerns expressed by the customer during the call.

Customer Profile:

Comprises historical interaction data, customer preferences, past purchases, and any predictive analytics that provide insights into customer behavior and needs.

Next-Best-Action Determination:

Pega CDH processes this combined data to generate the most relevant and timely action recommendations for the service representative to offer to the customer.

References and Supporting Documentation:

The Pega Customer Decision Hub User Guide outlines that the system uses both the call context and the customer profile to determine the Next-Best-Action in a contact center environment, ensuring personalized and contextually relevant interactions.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf

Pega-Customer-Decision-Hub-User-Guide-86.pdf

Pega-Customer-Decision-Hub-User-Guide-87.pdf

 Understanding Refreshable Segment Option:

This option ensures that the segment is updated automatically whenever it is referenced.

 Functionality:

When enabled, all segments that reference the refreshable segment are automatically updated, ensuring that the latest data is used.

 Purpose:

This helps maintain data accuracy and relevance across all segments and ensures that the decisioning process is based on current data.

 Verification from Pega Documentation:

Pega documentation on using and configuring refreshable segments in outbound schedules.

Identifying the Requirement:

The bank wants to leverage Pega CDH’s Next-Best-Action capability to promote new offers on their website.

The task is to determine what information Pega CDH sends back to the website in response to a real-time container request.

Evaluating Pega CDH Capabilities:

Pega CDH’s Next-Best-Action Designer is designed to handle real-time interactions and provides personalized recommendations.

The system returns comprehensive details to ensure the website can properly display and execute the recommended actions.

Process Overview:

Real-Time Container Request:

When the website sends a real-time container request, Pega CDH processes the request and identifies the most relevant actions based on customer data and engagement policies.

Response Details:

The response includes treatment details such as the action’s visual elements (image URLs, descriptions) and relevant action attributes (e.g., offer type, benefits, eligibility criteria).

References and Supporting Documentation:

The Pega Customer Decision Hub User Guide clearly outlines that in response to a real-time container request, the system sends back the treatment details and relevant action attributes to ensure the offer can be presented accurately on the website.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf

Pega-Customer-Decision-Hub-User-Guide-86.pdf

Pega-Customer-Decision-Hub-User-Guide-87.pdf​

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.

Real-Time Channels for Next-Best-Actions: Pega Customer Decision Hub supports presenting Next-Best-Actions through various real-time channels.

Retail Store: Actions can be presented to customers directly in the store through POS systems or store associates.

Call Center: Customer Service Representatives can receive real-time Next-Best-Action recommendations to present to customers during interactions.

SMS: Automated messages can be sent to customers with personalized Next-Best-Action offers via SMS.

 Volume Constraint Reset Interval: To meet the requirement of giving away phone accessories in batches of 50 and promoting a new batch once the stock in a batch is completed, you should select the "Manual" reset interval. This setting allows manual control over when the constraint is reset, enabling the business to start a new batch promotion as needed.

 Implementation: Configure the volume constraint for the promotional emails with the manual reset interval to ensure controlled batch processing.

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.

 Engagement Policy Condition: For offering a Fiber Optic Cable package only to customers in towns with fiber optic availability, the best engagement policy condition to use is "Eligibility." Eligibility conditions are used to ensure that only customers who meet specific criteria (e.g., geographic location) are considered for the offer.

 Implementation: Define an eligibility condition in the engagement policy that checks whether the customer resides in a town with fiber optic cable availability.

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.

Traditionally, segments were used to identify the target audience for a campaign. In the always-on approach, segments translate into engagement policies and AI. Engagement policies define when specific actions or groups of actions are appropriate for customers, while AI-driven decisions help determine the most relevant next best actions for individual customers based on predictive and adaptive analytics. This approach ensures that each customer interaction is highly personalized and contextually relevant.

 Pega allows for the personalization of email subjects using customer data and expressions.

 This feature enhances the relevance and engagement of the email content.

 For example, using "Dear " in the subject line can increase open rates and customer engagement.

 Reference: Pega-Customer-Decision-Hub-User-Guide-87.pdf, section on configuring email treatments and personalizing content .

To analyze the distribution of offers to well-engaged, under-engaged, or not-engaged customers, you would run the Pega Value Finder simulation. The Value Finder tool is designed to help identify and understand the engagement levels of different customer segments.

Pega Value Finder:

Pega Value Finder is used to identify under-served or over-served customers by analyzing historical interaction data.

It provides insights into how well different customer segments are being served and helps in understanding engagement levels.

This tool can categorize customers into well-engaged, under-engaged, or not-engaged based on their interactions and responses to offers.

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.

 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.

In a call-center application that receives Next-Best-Actions from Pega Customer Decision Hub, the customer service representative (CSR) is guided on the next important conversation to have with the customer. The Next-Best-Action recommendations help CSRs make informed decisions and provide relevant, personalized service to customers by suggesting the most appropriate actions based on the customer's profile, interaction history, and current context.

Proposition Data Components - These components in a decision strategy are used to import and reference actions or propositions.

Requirement - The strategy designer wants to import all actions from one group and a subset from another.

Minimum Number Calculation:

One component for importing all 10 actions from the Sales/Credit Cards group.

Another component for importing the 2 specific actions from the Sales/Mortgages group.

Compute SUM of PROFIT = Group BY

Determine target budget to retain a customer = Set Property

Determine customer credit score = Scorecard

Divide customers into risk segment = Decision Table

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.

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 .

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.

 Propensity Increase: In Pega Customer Decision Hub, propensity indicates the likelihood of a customer responding positively to an offer. A propensity value of 0.83 signifies a high likelihood of customers showing interest in the Gold credit card offer.

 Customer Behavior Analysis: The increase to 0.83 suggests that similar customers have demonstrated interest, such as clicking on the offer or engaging with it in some way.

In Pega Customer Decision Hub, the property that is automatically recomputed for each decision component is "Priority." This property determines the order in which actions are evaluated and presented to the customer. The Priority property ensures that the most relevant and valuable actions are prioritized based on the defined criteria, including customer relevance, business rules, and real-time contextual data.

 Requirement for Total Revenue Calculation:

MyCo needs to calculate the total revenue of three new actions over a specified period.

The goal is to aggregate the revenue generated by these actions.

 Use of Group By Component:

The Group By component is used to aggregate data based on specified properties.

In this scenario, it can be configured to sum the revenue of the three new actions.

 Configuration Steps:

Configure a single Group By component to group the actions by their identifiers.

Use the aggregation function within the Group By component to calculate the sum of the revenue for the grouped actions.

 Efficiency and Simplicity:

Using a single Group By component simplifies the strategy by avoiding redundancy.

It ensures that all actions are considered in a single aggregation process, providing an accurate total revenue calculation.

 Verification from Pega Documentation:

According to Pega's guidelines, a single Group By component can efficiently compute total values such as revenue by grouping and aggregating data.

To prioritize mortgages over credit cards for the fourth quarter, you should configure the business levers in Pega Customer Decision Hub. Business levers allow you to adjust the weight or priority of different actions based on business goals. By increasing the weight for mortgage offers, you can ensure that these offers are prioritized over credit card offers, even if a credit card may create more value.

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.

 Volume Constraints Implementation: To implement a policy that limits the sending of more than 500 emails per day, you need to configure volume constraints. Volume constraints allow you to set limits on the number of actions delivered within a specified period.

 Configuration: Create a volume constraint rule and set the maximum volume to 500 emails per day.

 Understanding Refresh the Audience Option:

This option ensures the decision process uses the most current data available.

 Functionality:

Before each run of the schedule, the audience data is refreshed to reflect any changes or updates in customer information.

 Purpose:

This ensures decisions are made based on the latest customer profiles, improving the relevance and accuracy of recommendations.

 Verification from Pega Documentation:

Pega documentation on scheduling and audience refresh options for ensuring up-to-date customer data is used.

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 use the same action flow template with the desired flow pattern for all credit card actions, you should configure a dynamic template.

Understanding Dynamic Templates:

Dynamic Templates: These templates allow you to define a reusable flow pattern that can be applied to multiple actions. They support dynamic configuration of treatments and other flow elements.

Configuration Steps:

Step 1: Create a dynamic template in Pega Customer Decision Hub.

Step 2: Define the flow pattern within the dynamic template, including all necessary steps and treatments.

Step 3: Apply the dynamic template to each credit card action, ensuring that the flow pattern is consistent across all actions.

Benefits:

Using dynamic templates ensures consistency in the flow patterns and simplifies the maintenance process.

Any updates to the flow pattern can be made in the template and will automatically apply to all actions using that template.

Example:

U+ Bank configures a dynamic template for their promotional email flows. This template includes steps for sending the email, waiting for a response, and following up as needed. Each credit card action uses this dynamic template to ensure a consistent customer experience.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Creating and using dynamic templates" section.

Pega documentation on "Action flow templates and dynamic configuration".

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.

In response to the real-time container request by Customer Decision Hub, the U+ Bank website receives only relevant action details and its treatment details. This information includes the specifics of the action to be displayed (such as the offer) and the associated treatment (such as the banner or message), enabling the website to present personalized and contextually relevant offers to customers.

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.

Identifying the Requirement:

The bank has chosen an email service provider that prefers receiving customer batches through a cloud storage location on either Microsoft Azure or Amazon S3.

The task is to find out how Pega Customer Decision Hub can integrate with these cloud storage solutions.

Evaluating Pega CDH Capabilities:

Pega Customer Decision Hub provides robust capabilities for integrating with various external systems and cloud storage solutions.

The documentation for Pega CDH supports writing action details directly to cloud storage solutions like Microsoft Azure and Amazon S3, which is in line with modern cloud infrastructure practices.

Process Overview:

Configuration:

Configure the external storage location (Azure Blob Storage or Amazon S3) within Pega CDH.

Action Flow:

Define the action flow to include steps for writing data to the specified cloud storage.

External Integration:

Use built-in connectors or custom integration points to handle data transfer securely and efficiently.

References and Supporting Documentation:

According to the Pega Customer Decision Hub User Guide, you can configure Pega CDH to write action details directly to cloud storage solutions like Microsoft Azure and Amazon S3 (refer to the section on configuring the delivery of multiple treatments and external integration points).

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf

Pega-Customer-Decision-Hub-User-Guide-86.pdf

Pega-Customer-Decision-Hub-User-Guide-87.pdf​

 Identify the issue: When a customer receives an offer they have already accepted, it suggests that the decision strategy did not consider previous interactions.

 Check strategy customization: Strategies in Pega CDH can be customized to include rules that prevent the same offer from being presented again.

 Confirm the exclusion of previous offers: Specifically, you need to ensure that there is a suppression rule or a logic to check the Interaction History to exclude previously accepted offers.

 Reference: Refer to the section on "Next-Best-Action Designer constraints" and "Engagement policies" in the Pega Customer Decision Hub User Guide. These sections explain how to configure rules to prevent repetitive offers​​.

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.

 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​​.

Designing Reusable Action Flows:

When creating an action flow intended for reuse, it's important to configure it with specific treatments.

The Specify Treatment option allows the flow to dynamically apply the appropriate treatment based on the action context.

This approach ensures consistency and flexibility, making it easier to manage and update the flows.

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.