Pegasystems PEGACPDC25V1 Certified Pega Decisioning Consultant 25 Exam Practice Test

Using the decision table, you can find the label for a customer with a credit score of 240 and an average balance of 35000 by following these steps:

Start from the top row and check if the customer’s credit score is less than 150. If yes, then the label is Very Poor. If no, then move to the next row.

Check if the customer’s credit score is less than 175 and their average balance is less than 25000. If yes, then the label is Poor. If no, then move to the next row.

Check if the customer’s credit score is less than 200 and their average balance is less than 50000. If yes, then the label is Fair. If no, then move to the next row.

Check if the customer’s credit score is less than 250 and their average balance is less than 75000. If yes, then the label is Good. If no, then move to the last row.

The last row applies to all other cases that do not match any of the previous conditions. The label for this row is Very Poor.

In this case, the customer’s credit score is not less than 150, so the first row does not apply. The customer’s credit score is less than 175, but their average balance is not less than 25000, so the second row does not apply either. The customer’s credit score is not less than 200, so the third row does not apply. The customer’s credit score is less than 250 and their average balance is less than 75000, so the fourth row applies. Therefore, the label for this customer is Poor.

Paul cannot see the Reward card offer because he rejected other credit card offers twice on the web channel and once in contact center in the past 15 days. This triggers the first contact policy that suppresses a group of credit card offers for 30 days if any credit card offer is rejected three times in any channel in the past 15 days. The Reward card offer is part of the credit card group, so it is suppressed for Paul for 30 days. The second contact policy that suppresses the Reward card offer for 7 days if it is rejected twice in any channel in the last 7 days does not apply because Paul did not reject the Reward card offer twice in any channel in the last 7 days. Verified References: [Certified Pega Decisioning Consultant | Pega Academy], Suppression policies system Following is the description of the image that was sent with question no:5:

This is a screenshot of a table with four columns and two rows.

The table has a header row with white text on a blue background.

The header row reads “Constraint name”, “Constraint mode”, “Constraint value”, and “Channel”.

The second row has black text on a white background.

The second row reads “Standard card”, “Return any action that does not exceed”, “100”, and “Daily”.

The table has a gray border and a light blue background.

A screenshot of a survey AI-generated content may be incorrect.

The score calculation is independent of the strategy and no change is required. When you use a scorecard component in a decision strategy, you only need to specify the name of the scorecard rule and the output property that will store the score value. The scorecard rule itself defines how the score is calculated based on the input properties and factors. Therefore, if you update the scorecard rule to include a new property in the calculation, you do not need to make any changes in the decision strategy for the updated scorecard to take effect. Verified References: [Pega Academy - Decisioning Consultant - Using scorecards]

The Next-Best-Action strategy is a customer-centric approach that aims to deliver the most relevant and valuable proposition for each customer at any given moment. You can use the Next-Best-Action strategy to address various business issues that involve customer interactions, such as service, retention, and collections. Service is the process of providing assistance and support to customers who have questions or problems. Retention is the process of preventing customers from leaving or switching to competitors. Collections is the process of recovering unpaid debts from customers who are delinquent or defaulting on their obligations. These are all business issues that can benefit from using the Next-Best-Action strategy. Verified References: [Pega Decisioning Consultant | Pega Academy]

To test the visualization and the rendering of the email content, including replacing of the placeholders with customer information, you use a seed list from the Test email tab. A seed list is a predefined set of customers that you can use to test your email treatments before sending them to your target audience. You can select one or more customers from the seed list and send them a test email with your treatment. You can then verify how the email looks in their inbox and how the placeholders are replaced with their actual values. Verified References: [Pega Academy - Decisioning Consultant - Testing email treatments]

Propensity is the name of the property that the system computes automatically when you use an Adaptive Model decision component. Propensity is a measure of how likely a customer is to accept an action, based on their past behavior and profile. An Adaptive Model component uses machine learning to calculate the propensity for each action and store it in a property with the same name as the action. Verified References: Pega Academy - Decisioning Consultant - Using adaptive models

To implement this requirement, you need to create an external email treatment and use an external tool to design the email. An external email treatment is a type of treatment that allows you to use a third-party email service provider to send emails to your customers. You can configure the external email treatment with the name of the provider, the authentication details, and the template ID that corresponds to the email content that you design using an external tool. This way, you can leverage your existing tools and processes for sending emails, while still using Customer Decision Hub to select the best actions and treatments for your customers. Verified References: Pega Academy - Decisioning Consultant - Creating external treatments

To output the most profitable shoe, you need to add a Prioritize component in the blank space. A Prioritize component allows you to rank actions based on one or more properties. In this case, you can rank the shoes based on the Profit property and select the highest ranked shoe as the output. Verified References: Pega Academy - Decisioning Consultant - Prioritizing actions

The business weight is a parameter that allows you to manually adjust the priority of an offer based on your business objectives and preferences. The business weight is multiplied by the business value and the propensity to calculate the final priority score of an offer. A higher business weight means a higher priority score, making the offer more likely to be selected and presented to the customer. Therefore, if you want to boost an offer, you can increase its business weight. Verified References: Pega Decisioning Consultant | Pega Academy

The always-on outbound customer engagement paradigm relies on AI to select the best action for each customer at any given time, based on their profile, context, and behavior. You do not need to create separate segments or schedules for different offers or timings. You can configure the primary schedule to run daily and let the AI choose the best action based on engagement policies, such as contact policies, eligibility rules, suitability rules, and arbitration. The AI will also learn from the customer responses and optimize the action selection over time.

A customer might receive an action that they already accepted if the volume constraint for that action is not configured to exclude previously accepted offers. This option can be enabled by selecting the Exclude previously accepted actions checkbox in the volume constraint configuration. The action suitability conditions are used to determine whether an action is suitable for a customer based on their propensity, priority, or other criteria, not based on their previous responses. The suppression rules are used to exclude customers from receiving an action based on certain conditions, such as opt-out preferences or recent purchases, not based on their previous responses. The actions are filtered based on eligibility before applying the volume constraints, so this option does not explain why a customer might receive an action that they already accepted. Verified References: Certified Pega Decisioning Consultant | Pega Academy, Volume constraints

The outbound run delivers 75 emails and 25 SMSes for the Standard card offer because the volume constraint is set to limit the number of actions per channel per day. The email channel has a limit of 75 actions per day, so all 75 email offers are delivered. The SMS channel has a limit of 25 actions per day, so only 25 SMS offers are delivered. The remaining 50 SMS offers are not delivered because they exceed the volume constraint.

To achieve this requirement, you need to ensure that two tasks are complete in the Channel tab of the Next-Best-Action Designer: enable a web channel and map the real-time container to a business structure level. A web channel is a type of channel that allows you to deliver actions and treatments to your customers through your website or web app. You need to enable a web channel and configure its settings, such as authentication, security, and response format. A real-time container is a configuration that defines how to deliver actions and treatments to a specific channel. You need to map the real-time container that contains the Reward Card offer to a business structure level, such as Top-level or Issue-level, so that it can be available for delivery through the web channel. Verified References: [Pega Academy - Decisioning Consultant - Configuring channels], [Pega Academy - Decisioning Consultant - Configuring real-time containers]

To access a property from an unconnected component, you use the component-dot-property construct. For example, if you want to access the property .Rank from an unconnected component named ActionRanking, you use ActionRanking.Rank. Verified References: Pega Academy - Decisioning Consultant - Accessing properties from unconnected components