Free Pega PCDS PEGACPDS88V1 Ultimate Study Guide (Updated 142 Questions) [Q36-Q56]

Published date December 22, 2023

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Free Pega PCDS PEGACPDS88V1 Ultimate Study Guide (Updated 142 Questions)

Get to the Top with PEGACPDS88V1 Practice Exam Questions

Pegasystems PEGACPDS88V1 (Certified Pega Data Scientist 88V1) Certification Exam is a globally recognized certification course designed to identify individuals who possess the skills and knowledge required to become a Pega Certified Data Scientist. Certified Pega Data Scientist 88V1 certification course is ideal for individuals who want to validate their proficiency in Pega’s data science platform, which includes predictive analytics, machine learning algorithms, and data visualization tools.

Pegasystems PEGACPDS88V1 certification exam is designed to test the knowledge and skills of individuals who want to become certified Pega Data Scientists. Pega Data Scientists are professionals who have the ability to use Pega’s predictive and adaptive analytics tools to analyze and transform data into insights that can drive business decisions. PEGACPDS88V1 exam covers a wide range of topics including statistical modeling, data visualization, machine learning, and data management. PEGACPDS88V1 exam is designed to be challenging and comprehensive, ensuring that only the most qualified individuals can earn the certification.

NEW QUESTION 36
What is the most accurate description of proactive retention? Proactive Retention_______

simplifies the process of retaining customers

enables business to respond to customers when they contact a call center

anticipates potential customer churn

enables the business to reduce the number of credit risk customers

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

NEW QUESTION 37
In a strategy defined in the “Retension” issue and the “X-Sell” group, you can import________

Actions from all groups under the “Retention” issue

Actions from “X-Sell” group

Actions from the “Sales” issue

All active actions

Explanation
According to the Pega Academy1, a strategy is a unit of reasoning that defines how to select an action for a customer. A strategy can be defined in different issues and groups, which are categories that help organize actions. An issue represents a business goal (such as retention or sales), and a group represents a subcategory of an issue (such as cross-sell or up-sell).
In a strategy defined in the “Retention” issue and the “X-Sell” group, you can import actions from all groups under the “Retention” issue1. This allows you to use actions that are relevant to your business goal and compare them with other actions in the same issue.

NEW QUESTION 38
A best practice in data science is to use a control group. What business metric is supported by this practice?

The lift that the prediction generates

The number of responses

The performance of the prediction

The success rate of the prediction

Explanation
The lift that the prediction generates Reference:
Using a control group is a best practice in data science that supports the business metric of the lift that the prediction generates.

NEW QUESTION 39
Which statement about predictive models is true?

Predictive models need historical data to be created

Predictive models need to be specified in a data attribute

Predictive models are always associated with an action

Predictive models need unstructured bie data

Explanation
Predictive models need historical data to be created. Predictive models are statistical models that use historical data to learn patterns and trends and make predictions for future outcomes. Predictive models can be built with Pega machine learning or imported from third-party tools such as PMML or H2O. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision

NEW QUESTION 40
In a decision strategy, the Adaptive Model decision component belongs the

Decision Analytics category

Business Rules category

Arbitration category

Predictive Model category

Explanation
In a decision strategy, the Adaptive Model decision component belongs to the Decision Analytics category.
This category contains components that use advanced analytics techniques, such as adaptive models, predictive models, text analytics models, etc., to make predictions or recommendations. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/decision-analyt

NEW QUESTION 41
When implementing Next-Best-Action, the Customer Lifetime Value Threshold is typically used to_________.

prioritize high value propositions

prioritize customers

determine if the customer is eligible

calculate the customer’s lifetime value

Explanation
The Customer Lifetime Value Threshold is typically used to prioritize customers based on their expected long-term value to the business. Customers who have a higher lifetime value than the threshold are considered high-value customers and receive more personalized and relevant offers. References:
https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

NEW QUESTION 42
A contact center application recommends relevant actions for each customer. The business team wants to know the possible ways in which these actions can be ordered so that the contact center agent can discuss one proposition at a time, starting from the top.
As a strategy designer, what are your two options if you use a Prioritize component to order the actions?
(Choose Two)

In a random order

In alphabetical order based on the action name

In descending order based on a numerical value

In ascending order based on a numerical value

Explanation
The prioritize component is used to order actions based on a numerical value, such as priority, propensity, or custom expression. You can choose to sort the actions in descending or ascending order. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/prioritizing-act

NEW QUESTION 43
When you build a decision strategy, what property do you use to access the output of a prediction that is driven by a predictive model markup language (PMML) model?

pxEvidence

pxResult

pxSegment

nxOutcome

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

NEW QUESTION 44
The purpose of model templates when using Pega machine learning is

to set the model outcomes

to set the model context

to streamline model deployment

to streamline model development

Explanation
The purpose of model templates when using Pega machine learning is to streamline model development.

NEW QUESTION 45
U+ Bank, a retail bank, offers the Standard card, the Rewards card and the Rewards Plus card to its customers.
The bank wants to display the banner for the offer that each customer is most likely to click; therefore, their Arbitration uses Propensity from the AI models. If you are debugging the Next-Best-Action decision strategy, which strategy component will show you if the result of the Arbitration is correct?

Filter

Group By

Set Property

Prioritize

Explanation
If you are debugging the Next-Best-Action decision strategy and want to see if the result of the Arbitration is correct, you should use the Prioritize strategy component.

NEW QUESTION 46
Predictions combine predictive analytics and best practices in data science. Which two best practices are included in Pega Customer Decision Hub predictions? (Choose Two)

Setting the response timeout

Using a control group

Selecting the model with the highest performance

Selecting the audience

Explanation
Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub predictions are using a control group and selecting the model with the highest performance.

NEW QUESTION 47
The likelihood that an action will be accepted by the customer is stored in the Strategy property called_______

pyBehavior

pyProbability

pyLikelihood

pyPropensity

Explanation
The pyPropensity property stores the likelihood that an action will be accepted by the customer. It is calculated by a predictive model or an adaptive model and used in decision strategies to prioritize actions. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/using-predictio

NEW QUESTION 48
What are the most important aspects taken into consideration when determining the Next-Best-Action?

Product discounts and business profitability

Network bandwidth and call duration

Market trends and customer satisfaction

Business objectives and customer needs

Explanation
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 preferences and expectations of the customers, such as their interests, intents, or life events. References:
https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

NEW QUESTION 49
An online store is interested in increasing its revenues from cross-selling and wants to predict the acceptance rate of the offers presented on their website. A customer’s propensity to accept an offer increases when_________.

Similar offers were rejected by the customer

The offer was rejected by similar customers

Similar offers were accepted by the customer

The offer was accepted by similar customers

Explanation
This is because a customer’s propensity to accept an offer depends on their past behavior and preferences. If a customer has accepted similar offers in the past, they are more likely to accept a new offer that matches their interests
https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuide.pdf

NEW QUESTION 50
A very important aspect of each model is how good a model or a given predictor is in predicting the required behavior. When building a predictive model, the use of testing and validation samples___________________

increases the accuracy of models

validates the quality of input data

enables model validation in strategies

is mandatory for segmentation

Explanation
A predictive model is a mathematical function that estimates the probability of an outcome based on input data. When building a predictive model, the use of testing and validation samples increases the accuracy of models123. Testing and validation samples are subsets of data that are used to evaluate how well a model performs on new data that was not used to train the model. Testing and validation samples help prevent overfitting, which is when a model learns too much from the training data and fails to generalize to new data.

NEW QUESTION 51
When compared to a Predictive Model, an Adaptive Model is different as it_____________

can use strategy properties as predictors

considers both symbolic and numeric predictors

learns from both positive and negative outcomes

uses predictor binning

Explanation
An adaptive model is different from a predictive model as it can use strategy properties as predictors. Strategy properties are dynamic values that are calculated or derived during the execution of a decision strategy. They can capture customer context, such as channel, location, time, etc. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-m

NEW QUESTION 52
The management team at U+ Insurance wants to improve the experience of dissatisfied customers. The customers send the feedback through email.
To detect the sentiment of the incoming emails, which type of prediction do you need to configure in Prediction Studio?

Pega Customer Decision Hub prediction.

Sentiment detection does not require any predictions.

Case management prediction.

Text analytics prediction.

Explanation
To detect the sentiment of the incoming emails, you need to configure a text analytics prediction1234 in Prediction Studio. A text analytics prediction is a type of prediction that uses natural language processing (NLP) to analyze text data and extract insights, such as topics, entities, and sentiments. You can use a text analytics prediction to detect the sentiment of an email based on its content and assign a score ranging from -1 (negative) to 1 (positive). This can help you improve the customer experience by identifying dissatisfied customers and taking appropriate actions.

NEW QUESTION 53
To create channel-specific Adaptive Model instances, you____________

do nothing; Adaptive Model instances are always channel specific

create channel specific Adaptive Model definition

set channel information in the strategy

set the channel option in the Adaptive Model component

NEW QUESTION 54
When implementing a Next-Best-Action project, which step is recommended to be taken first?

Define Issue and Group hierarchy

Define propositions

Define business rules

Define prioritization formula

Explanation
When implementing a Next-Best-Action project, the recommended first step is to define Issue and Group hierarchy, which are used to organize and categorize propositions based on business objectives and customer needs. This step helps to align the project with the business vision and goals. References:
https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

NEW QUESTION 55
The filter component is used to filter_______

Actions

Customers

Adaptive models

attributes

Explanation
The filter component is used to filter actions based on various criteria, such as eligibility, suitability, priority, or custom conditions. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/filtering-action

NEW QUESTION 56
U+ Insurance uses Pega Process AI and wants straight-through processing of claims with a low fraud risk.
As a data scientist, you create a prediction that calculates the probability that a claim is fraudulent.
What type of prediction do you create to meet this requirement?

A case management prediction.

A text analytics prediction.

A Customer Decision Hub prediction.

A fraud detection prediction

Explanation
to create a prediction that calculates the probability that a claim is fraudulent, you need to create a case management prediction. This type of prediction allows you to use predictive models built on external platforms such as H2O.ai and apply them to case types in Pega Process AI. You can then use the prediction outcome in a decision step to route claims based on their fraud risk.
https://academy.pega.com/challenge/creating-fraud-prediction/v3

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