Free PEGACPDC88V1 Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

The propensity is a measure of how likely a customer is to accept an offer, based on their attributes

and behaviors. The propensity is calculated by using predictive analytics models that learn from

historical data and feedback. A low propensity value indicates that the offer is not relevant or

attractive for the customer, and that similar customers have ignored or rejected the offer in the past.

Therefore, if the offer click-through propensity decreased to 0.42, it most likely indicates that similar

customers ignore the offer. Verified Reference: Pega Decisioning Consultant | Pega Academy

The arbitration factor is a parameter that allows you to adjust the weight of each factor in the

prioritization expression, based on your business strategy and p

. The arbitration factor is

multiplied by the factor value to calculate the final priority score of each offer for each customer. If

you want to boost customers’ needs in the prioritization formula, you can increase the arbitration

factor for the propensity, which is the factor that reflects the predicted customer behavior. The

higher the arbitration factor for the propensity, the more influence it has on the priority score,

making the offers that match customers’ needs more likely to be selected and presented to the

customer. Verified Reference: [Pega Decisioning Consultant | Pega Academy]

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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 p

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

Reference: Certified Pega Decisioning Consultant | Pega Academy, Volume constraints

To present offers from the two groups, you must map a real-time container to the Top-level or Issue-

level. A real-time container is a configuration that defines how to deliver offers and treatments to a

specific channel, such as a website or a mobile app. By mapping a real-time container to the Top-

level or Issue-level, you can enable all the offers under that level to be available for delivery through

that channel. Verified Reference: Pega Academy - Decisioning Consultant - Configuring real-time

containers

Volume constraints allow you to limit the number of times an action is presented to customers across

one or more channels. You can use volume constraints to implement a policy that restricts the

number of emails sent per day. Outbound channel limits are used to limit the number of customers

contacted per channel per run, not per day. Suppression rules are used to exclude customers from

receiving an action based on certain conditions, such as opt-out p

or recent purchases.

Applicability rules are used to determine whether an action is relevant for a customer based on their

profile or context, not based on the number of times the action is presented. Verified

Reference: Certified Pega Decisioning Consultant | Pega Academy, Volume constraints

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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 Reference: [Pega Academy - Decisioning

Consultant - Testing email treatments]

Next-Best-Action is a type of decision that involves selecting and prioritizing the most appropriate

proposition for each customer at any given moment. Next-Best-Action can be applied to decisions

that require customer-centricity, personalization, and contextualization. Determining if a borrower

gets a loan is an example of such a decision, as it depends on the customer’s attributes, behaviors,

p

, and needs. The other options are examples of decisions that are not related to customer

interactions, but rather to operational or analytical processes. Verified Reference: [Pega Decisioning

Consultant | Pega Academy]

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 Reference: Pega Academy -

Decisioning Consultant - Creating external treatments

An engagement policy condition is a rule that determines whether a customer is eligible, applicable,

or suitable for an offer. You can use engagement policy conditions to filter customers based on their

attributes or behaviors. In this case, the bank wants to offer a Gold credit card to customers who

have an annual income of more than USD150000, so creating an engagement policy condition based

on the income attribute is the best option. Verified Reference: [Pega Decisioning Consultant | Pega

Academy]

Increasing stage upweighting is a feature that allows you to gradually increase the weight of a stage

over time, making the offers in that stage more likely to be selected. This is useful for promoting

offers that are time-sensitive or have a limited availability. In this case, the bank wants to actively

increase offers promotion over time, so enabling increasing stage upweighting for the second stage

of the journey, where the offers are presented, is the best option. Verified Reference: [Pega

Decisioning Consultant | Pega Academy]

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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 Reference: [Pega Academy -

Decisioning Consultant - Using scorecards]

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In a decision strategy, to use a customer property in an expression, you need to define Customer

page in Pages & Classes and specify its class as Data-Customer. This allows you to access customer

properties by using dot notation, such as Customer.Age or Customer.Gender. You do not need to

define the property as a strategy property, use it without any prefix, or prefix it with the keyword

Customer. Verified Reference: [Certified Pega Decisioning Consultant | Pega Academy], Decision

strategies