[Q15-Q39] Verified PEGACPDS88V1 dumps Q&As - Pass Guarantee Exam Dumps Test Engine [2023]

[Q15-Q39] Verified PEGACPDS88V1 dumps Q&As – Pass Guarantee Exam Dumps Test Engine [2023]

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Verified PEGACPDS88V1 dumps Q&As – Pass Guarantee Exam Dumps Test Engine [2023]

PEGACPDS88V1 dumps and 142 unique questions

The PEGACPDS88V1 exam covers a variety of topics related to Pega’s data analytics platform, including data preparation, data modeling, model evaluation, and deployment. Candidates must also be familiar with Pega’s specific tools and technologies, such as Pega Predictive Analytics Director and Pega Decision Management. To pass the exam, candidates must demonstrate their ability to apply these skills and concepts to real-world scenarios.

QUESTION 15
The implementation of Next-Best-Action must involve

inclusion of third party predictive models

defining business issue and group hierarchy

defining a prioritization formula using contact policies

building a product catalog

Explanation
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. References:
https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer

QUESTION 16
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

QUESTION 17
What is the difference between predictive and adaptive analytics?

Predictive models can predict a continuous value.

Predictive models predict customer behavior.

Adaptive models use the customer data as predict*

Predictive models have evidence.

Explanation
The difference between predictive and adaptive analytics is that adaptive models use the customer data as predictors, while predictive models use the customer data as outcomes. Adaptive models learn from real-time customer interactions and update their predictions accordingly. Predictive models use historical customer data to train and validate their predictions. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-m

QUESTION 18
A telecommunications company wants to apply text analysis to incoming emails to understand how satisfied its customers are with various products and services. That setup requires natural language processing (NLP) of the email texts. What is one of the types of analysis that occurs during natural language processing?

Presumptive analysis

Intent analysis

Subjective analysis

Semantic analysis

Explanation
One of the types of analysis that occurs during natural language processing is Semantic analysis.

QUESTION 19
The standardized model operations process (MLOps) lets you replace a low-performing predictive model that drives a prediction with a new one.
Which feature of MLOps lets you monitor the new model in the production environment without affecting the business outcomes?

Change request

Shadow mode

Historical data capture

Connection to machine learning services

Explanation
This is because shadow mode allows you to test a new model in parallel with an existing model without affecting the decision outcomes. You can compare the performance of both models and decide whether to replace or keep the existing model.
https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuide.pdf

QUESTION 20
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?

Case management_____

Customer Decision Hub

Text analytics

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.

QUESTION 21
In a decision strategy, to remove propositions based on the current month, you use a

Calendar component

Filter component

Data Strategy property

Calendar strategy property

Explanation
The calendar component is used to remove propositions based on the current month, day of week, or time of day. It can also be used to apply seasonal adjustments to propositions. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/using-calendar-

QUESTION 22
As a highly experienced data scientist, which two advanced settings are available to you? (Choose Two)

Outcomes

Predictor types

The parameters used to bin the responses

Predictor selection

The update frequency of the models

Explanation
Predictor selection and The update frequency of the models Reference:
As a highly experienced data scientist, you have access to advanced settings such as predictor selection and the update frequency of the models

QUESTION 23
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

QUESTION 24
The decision components use on the strategy canvas can be individually configured.
Which function is available when configuring the Group By component?

Multiply

Divide

True if Some

Count

Explanation
According to the Pega Academy1, decision strategies drive the next best action and comprise a unit of reasoning represented by decision components. You use the Proposition Data component to import actions into a strategy canvas. The sequence of the components in the canvas determines which action is selected for a customer.
The Group By component2 is used to group a list of ranked items based on a field and retain only one element in each group. The function available when configuring the Group By component is Count2, which returns the number of elements in each group.

QUESTION 25
You are a company with a new and unique product, and you want to offer it to the right customer.
Give the scenario, which rule type should you use?

Adaptive model

Decision table

Predictive model

Scorecard

Explanation
You are a company with a new and unique product, and you want to offer it to the right customer. Given the scenario, you should use an adaptive model rule type. An adaptive model rule type allows you to define the predictors and the outcome of the model and associate it with an action. An adaptive model learns from customer responses in real time and predicts the propensity of each customer to accept the action. An adaptive model is suitable for new products or markets where there is no historical data available. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision

QUESTION 26
Which statement about predictive models is true?

You need past experience to create a predictive model.

They need unstructured big data.

They are always associated with a proposition.

They need to be specified in a data attribute.

QUESTION 27
The adaptive model component in a decision strategy computes

A single propensity value for all actions

A unique accept rate for each action

A single accept rate for all actions

A propensity value for each action

Explanation
The adaptive model component in a decision strategy computes a propensity value for each 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

QUESTION 28
When building a model using Pega machine learning, the validation hold-out set is used to____________and to____________. (Choose Two)

select the best model

train the models_____________________

check for robustness of candidate models

compare their performance

analyze the performance characteristics of candidate models

Explanation
When building a model using Pega machine learning, the validation hold-out set is used to train the models and to check for robustness of candidate models.

QUESTION 29
To enable an assessment of its reliability, the Adaptive Model produces three outputs: Propensity, Performance and Evidence. The performance of an Adaptive Model that has not collected any evidence is_________.

1-0

null

0.5

0.0

Explanation
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. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-m

QUESTION 30
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)

Call center

Billboard on the company building

Retail store

SMS

Traditional television advertisements

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.

QUESTION 31
A company wants to create a data set that includes mock-up customer add which method is available to populate these fields?

Propensity

Bayesian

Monte Carlo

Import

Explanation
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. References:
https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

QUESTION 32
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

QUESTION 33
Through analysis of customer lifecycles, Next-Best-Action

anticipates retention issues

provides future sales reports

provides fulfillment services

identifies global sales targets

Explanation
Through analysis of customer lifecycles, Next-Best-Action anticipates retention issues and takes proactive actions to prevent customer churn. 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

QUESTION 34
A company wants to simulate decisions that requires large amounts of data. However, the organisation’s live data is inaccessible. Your advice is to use a Monte Carlo data set. The Monte Carlo method

enables the company to generate random data for most of its application needs

generates data that the company can use as input for adaptive decisioning

combines external data sets into a larger data set

makes the organization’s live data accessible

Explanation
The Monte Carlo method enables the company to generate data that simulates customer behavior and can be used as input for adaptive decisioning. The generated data is based on predefined probabilities and distributions that reflect realistic scenarios. References:
https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

QUESTION 35
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)

The value of only the active predictors

The outcome of the interaction

The model metadata

The propensity generated by the model

The value of all predictors

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.

QUESTION 36
To enable an assessment of its reliability the adaptive model produces four outputs: propensity,performance, evidence and positives.
The Performance of an adaptive model that has not collected any evidence yet is______.

100

Explanation
The performance of an adaptive model that has not collected any evidence yet is 50. This means that the model is not confident about its predictions and assigns equal probability to all actions. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision

QUESTION 37
Which decision component allows you to monitor the real-time performance of a third- party Churn Model?

Scorecard Model

Adaptive Model

PMML Model

Predictive Model

Explanation
A scorecard model is a type of predictive model that allows you to monitor the real-time performance of a third-party churn model. A scorecard model compares the predicted churn probability with the actual churn outcome and calculates a performance score for each customer segment. References:
https://academy.pega.com/module/predictive-analytics/topic/using-scorecard-models

QUESTION 38
Which statement about the PMML standard is correct?

The PMML standard is designed to facilitate the exchange of models between applications

The PMML standard can only be used to describe tree, scorecard and regression models.

The PMML standard is a proprietary standard

The PMML standard is designed to facilitate the exchange of scores between applications

Explanation
The PMML standard is designed to facilitate the exchange of models between applications.

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

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Pegasystems PEGACPDS88V1 exam is designed for individuals who want to become certified Pega Data Scientists. Pega is a leading provider of business process management (BPM) software solutions that help organizations streamline their operations and improve customer engagement. The PEGACPDS88V1 exam tests candidates on their ability to use Pega’s data science tools and techniques to solve real-world business problems.

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