Free PEGACPDS88V1 Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

Proactive retention is a strategy that anticipates potential customer churn and takes actions to

prevent it before it happens. It uses predictive analytics to identify customers who are at risk of

leaving

and

offers

them

incentives

or

solutions

to

retain

them.

Reference:

https://academy.pega.com/module/one-one-customer-engagement/topic/proactive-retention

The pxResult property is used to access the output of a prediction that is driven by a PMML model. It

contains the predicted value or class for each record in the input data set. Reference:

https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models

When you increase the performance threshold setting of an adaptive model rule, the number of

active predictors may decrease. The performance threshold is the minimum performance that a

predictor must have to be included in the model. If you increase this value, some predictors may not

meet

the

criteria

and

be

excluded

from

the

model.

Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/ruledecision-/rule-decision-adaptivemodel/main.htm

The rank property is automatically recomputed for each decision component. It indicates the order in

which the actions are presented to the customer, based on their priority and propensity. Reference:

https://academy.pega.com/module/creating-and-understanding-decision-strategiesarchived/topic/ranking-actions

The Monte Carlo method is a way to create a data set that includes mock-up customer data. You can

use this method to generate random values for customer properties, such as age, gender, income,

etc.,

based

on

predefined

probabilities

and

distributions.

Reference:

https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creatingmonte-carlo-data-set

A prediction helps in proactive retention by predicting the customer’s churn risk. A prediction is an

estimate of the likelihood of a future outcome based on historical data and statistical models. A

prediction can help identify customers who are at risk of leaving and target them with appropriate

actions

to

retain

them.

Reference:

https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm

According to the Data Scientist Student Guide1, page 18, the correct answer is B. A predictive

model and C. A control group. A predictive model is a mathematical representation of a real-world

process that can be used to predict an outcome based on input data. A control group is a subset of

customers who are not exposed to a treatment (such as an offer) and are used to measure the

effectiveness of the treatment by comparing their behavior with the treated group.

Two results of an adaptive model are propensity and performance. Propensity is the probability that

the customer will accept or respond to an offer. Performance is a measure of how well the adaptive

model

predicts

customer

behavior

over

time.

Reference:

https://academy.pega.com/module/predicting-customer-behavior-using-real-time-dataarchived/topic/adaptive-models-overview

These factors indicate how accurate and explainable the models are, which are key measures of

model health. The number of responses and decisions are related more to model usage rather than

health.

The implementation of Next-Best-Action must involve defining business issue and group hierarchy,

which are used to organize and categorize propositions based on business objectives and customer

needs. Reference: https://academy.pega.com/module/one-one-customer-engagement/topic/nextbest-action-designer

When an adaptive model has not collected any evidence, its performance is 0.5, which means that it

has no predictive power and is equivalent to a random guess. As more evidence is collected, the

performance can increase or decrease depending on how well the model predicts customer behavior.

Reference:

https://academy.pega.com/module/predicting-customer-behavior-using-real-time-dataarchived/topic/adaptive-models-overview

Customer Decision Hub Reference:

To optimize the click-through rate of a web banner, you need to create a Customer Decision

Hub prediction.

Pega AI is primarily based on self-learning models Reference:

Pega AI is primarily based on self-learning models.

The Proposition Distribution report is used to compare the results of two simulations.

The most important aspects taken into consideration when determining the Next-Best-Action are

business objectives and customer needs. Business objectives reflect the goals and priorities of the

organization, such as increasing revenue, reducing costs, or managing risk. Customer needs reflect

the p

and expectations of the customers, such as their interests, intents, or life events.

Reference:

https://academy.pega.com/module/one-one-customer-engagement/topic/next-bestaction-designer

Pega adaptive models learn about customer behavior in real time by analyzing the responses to each offer and updating their predictions accordingly. They do not require historical data, human effort, or inbound channels to function. Reference: https://academy.pega.com/module/predicting-customerbehavior-using-real-time-data-archived/topic/adaptive-models-overview

The outcome of a scoring model indicates the likely response to an offer that is presented to a

customer. For example, a scoring model can predict if a customer will accept, reject, or defer an offer

for

a

credit

card

upgrade.

Reference:

https://academy.pega.com/module/predictiveanalytics/topic/using-scoring-models

Up to 100 fields to limit the impact on model speed Reference:

When configuring an adaptive model, it is recommended to select up to 100 potential predictors to

limit the impact on model speed.

Adaptive model predictors are selected from the customer profile, which contains information about

the customer’s attributes and behavior. Predictors can be either scalar or aggregate properties that

capture

customer

context,

such

as

channel,

location,

time,

etc.

Reference:

https://academy.pega.com/module/predicting-customer-behavior-using-real-time-dataarchived/topic/configuring-adaptive-models

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and

income Reference:

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and

income require no preprocessing before being used as potential predictors in adaptive analytics.