PEGACPDS88V1 Certified Pega Data Scientist 8 8 Dumps

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Share some PEGACPDS88V1 exam online questions below. 
1.As a data scientist, you are tasked with creating a new prediction that estimates a customers'
likelihood to leave the business in the near future. The NBA analyst wants to move forward and use
the prediction in Pega Customer Decision Hub™ to test the application.
To unblock the NBA specialist, which task do you prioritize?
A. Create the prediction
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B. Create the customer data model
C. Create a placeholder scorecard to drive the prediction
D. Create the predictive model that drives the prediction
Answer: D
Explanation:
To unblock the NBA specialist, as a data scientist, you should prioritize creating the predictive model
that drives the prediction.
2.Which two factors do you inspect to access the general health of the adaptive models in Prediction
Studio? (Choose Two)
A. Performance of the models
B. Number of responses
C. Number of decisions
D. Model transparency
Answer: A,D
Explanation:
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.
3.Evidence an assessment of its viability, the Adaptive Model produces three outputs: Propensity,
Performance and what is evidence in the context of an Adaptive Model? Performance and what is
evidence in the context of an Adaptive Model?
A. The likelihood of a statistically similar behavior
B. The number of customers who exhibited statistically similar behavior
C. The number of customers who have responded to the modeled offer
D. The number of statistical bins used to evaluate the response
Answer: B
Explanation:
Evidence is the number of customers who exhibited statistically similar behavior to the current
customer and responded to the modeled offer. It indicates how reliable the propensity score is based
on the available data.
References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-
archived/topic/adaptive-models-overview
4.Predictions combine predictive analytics and best practices in data science. As a data scientist,
what is a valid reason to adjust the default response timeout in a prediction?
A. Suit the use case
B. Optimize the success rate
C. Increase lift
D. Limit the number of responses
Answer: A
Explanation:
As a data scientist, a valid reason to adjust the default response timeout in a prediction is to suit the
use case.
5.The use of an imported third-party model in a decision strategy is____
A. Only possible after conversion into a Pega machine learning model
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B. Identical to the use of an adaptive model
C. Similar to the use of a model built with Pega machine learning
D. Only possible after conversion into Pega markup language
Answer: C
Explanation:
The use of an imported third-party model in a decision strategy is similar to the use of a model built
with Pega machine learning. You can use a predictive model component in a decision strategy to
reference an imported third-party model and pass the input parameters and receive the output score.
You do not need to convert the third-party model into a Pega machine learning model or Pega
markup language.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-predictivemodel/main.htm
6.The purpose of regular inspection is to detect factors that negatively influence the performance of
the adaptive models and the success rate of the actions.
Which two issues should be discussed with the business? (Choose Two)
A. Predictors with a low performance_________
B. Actions that have a low number of responses
C. Actions that are offered so often that they dominate other actions
D. Predictors that are never used
E. Actions for which the model is not predictive
Answer: A,C
Explanation:
When performing regular inspection of adaptive models, two issues that should be discussed with the
business are predictors with a low performance and actions that are offered so often that they
dominate other actions.
7.Acquiring new customers can be more costly than retaining active customers. U+ Bank uses Pega
Customer Decision Hub for its customer engagement and wants to reduce the churn rate by
identifying high churn risk customers and making them a retention offer.
To meet this requirement, which two artifacts created by a data scientist allow the NBA specialist to
implement the decision strategy? (Choose Two)
A. A prediction
B. A predictive model
C. A control group
D. An adaptive model
Answer: B,C
Explanation:
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.
8.The mapping of the input fields of a third-party predictive model is done in the
A. Predictive Model decision component
B. Predictive Model rule
C. Predictive Analytics Director portal
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D. Customer class definition
Answer: B
Explanation:
The mapping of the input fields of a third-party predictive model is done in the Predictive Model rule.
The Predictive Model rule defines how to invoke and interpret the results of a third-party predictive
model that is imported in PMML format.
References: https://academy.pega.com/module/predictive-analytics/topic/using-pmml-models
9.Pega Adaptive Models_________
A. involve a significant human effort to develop
B. require historical data_________________
C. learn about customer behavior in real time
D. can only be used in inbound channels
Answer: C
Explanation:
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.
References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-
archived/topic/adaptive-models-overview
10.MyCo, a telecommunications company, wants to implement one-to-one customer engagement
using Pega Customer Decision Hub™.
Which three of the following real-time channels can the company use to present Next-Best-Actions?
(Choose Three)
A. Call center
B. Billboard on the company building
C. Retail store
D. SMS
E. Traditional television advertisements
Answer: A,C,D
Explanation:Call center, SMS, and Retail store
Reference:
MyCo can use Call center, SMS, and Retail store as real-time channels to present Next-Best-Actions.
11.As a data scientist, you have enabled capturing of historical data in an adaptive model.
Which two data elements are captured for every customer interaction? (Choose Two)
A. The value of only the active predictors
B. The outcome of the interaction
C. The model metadata
D. The propensity generated by the model
E. The value of all predictors
Answer: B,E
Explanation:
When capturing historical data in an adaptive model, the outcome of the interaction and the value of
all predictors are captured for every customer interaction.
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12.U+ Telecom wants to engage in proactive retention to reduce churn. As a data scientist, you
create a prediction that calculates the probability that a client is likely to cancel a subscription.
What type of prediction do you create?
A. Case management_____
B. Customer Decision Hub
C. Text analytics
Answer: B
Explanation:
As a data scientist, you create a prediction that calculates the probability that a client is likely to
cancel a subscription. The type of prediction you create is Customer Decision Hub.
13.Adaptive model components can output__________
A. An option___________
B. An optimized strategy
C. The number of customer's eligible for an action
D. The customer's propensity to accept an action
Answer: D
Explanation:
Adaptive model components can output the customer’s propensity to accept an action. Propensity is
the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-
decision-/rule-decision-adaptivemodel/main.htm
14.U+ Bank promotes credit card offers on its website and uses Pega Customer Decision Hub to
personalize the offer for every customer. Now, the bank wants to lower the number of customers that
leave the bank by showing a proactive retention offer to high churn risk customers instead. As an
NBA analyst, you are tasked with creating a new applicability setting to comply with the new business
rule.
Which business issue or issues do you modify?
A. The Retention issue
B. The Sales issue
C. The Sales issue and the Retention issue
D. No modification is required
Answer: A
Explanation:
To comply with the new business rule of showing a proactive retention offer to high churn risk
customers, you should modify the Retention issue.
15.1.A Scoring Model allows you to differentiate between
A. Accept, Reject, Maybe Later
B. Good, Bad
C. Good, Better, Best
D. Good, Bad, Unknown
Answer: C
Explanation:
A scoring model allows you to differentiate between Good, Better, and Best outcomes for a given
proposition or action. A scoring model assigns a numerical value to each outcome based on its
desirability or profitability for the business.
References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
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16.As a data scientist, you are tasked with configuring two predictions that are driven by an adaptive
model: one for an inbound channel and one for an outbound channel.
To which setting do you need to pay extra attention?
A. Response timeout
B. Adaptive model
C. Predictor fields
D. Control group
Answer: B
Explanation:
As a data scientist, if you are tasked with configuring two predictions that are driven by an adaptive
model, you need to pay extra attention to adaptive model settings.
17.When developing a predictive model, the outcome value of a continuous model type can
represent__________________
A. customer churn
B. acceptance of an offer
C. customer loan default
D. the purchase value of an offer
Answer: D
Explanation:
When developing a predictive model, the outcome value of a continuous model type can represent
the purchase value of an offer.
18.What is the most accurate description of proactive retention? Proactive Retention_______
A. simplifies the process of retaining customers
B. enables business to respond to customers when they contact a call center
C. anticipates potential customer churn
D. enables the business to reduce the number of credit risk customers
Answer: C
Explanation:
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.
References: https://academy.pega.com/module/one-one-customer-engagement/topic/proactive-
retention
19.To create channel-specific Adaptive Model instances, you____________
A. do nothing; Adaptive Model instances are always channel specific
B. create channel specific Adaptive Model definition
C. set channel information in the strategy
D. set the channel option in the Adaptive Model component
Answer: D
20.Prediction Studio supports keyword-based topic detection, model-based topic detection, or a
combination of both. When using a text prediction based on machine learning with keywords
configured,_________________.
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A. the Not keywords function as negative features
B. the keywords are ignored
C. the Must keywords are required to detect the topic
D. keywords and training data have a similar impact on the model
Answer: A
Explanation:
When using a text prediction based on machine learning with keywords configured, the Not keywords
function as negative features, meaning that they reduce the probability of detecting the topic if they
appear in the text. The Must keywords and May keywords do not have any impact on the machine
learning model.
References: https://academy.pega.com/module/text-analytics/topic/configuring-keywords
21.U+ Bank introduces a new credit card that has no historical customer behavior data. U+ Bank
wants to offer this credit card on the personalized web portal. Given the scenario, which rule type
must you use?
A. Decision table
B. When rule
C. Adaptive model
D. Pega machine learning model
Answer: C
Explanation:
Given the scenario where U+ Bank introduces a new credit card that has no historical customer
behavior data and wants to offer this credit card on the personalized web portal, you must use an
adaptive model.
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