Question 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?

Accepted Answer

D

Explanation: To unblock the NBA specialist, as a data scientist, you should prioritize creating the predictive model that drives the prediction.

Question 2

When developing a predictive model, the outcome value of a continuous model type can represent__________________

Accepted Answer

D

Explanation: When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.

Question 3

Adaptive model predictors are selected from the____________.

Accepted Answer

C

Explanation: 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

Question 4

The Pega Customer Decision Hub™ delivers Next-Best-Actions consistently through multiple channels. To which of the following channels does this apply?

Accepted Answer

C

Explanation: The Pega Customer Decision Hub™ delivers Next-Best-Actions consistently through multiple channels. This applies to Mobile channels.

Question 5

What is the key component of a Next-Best-Action strategy?

Accepted Answer

A

Explanation: The key component of a Next-Best-Action strategy is a strategy, which is a graphical representation of the business logic that determines which actions to offer to each customer and in what order. A strategy can use various components, such as business rules, predictive models, filters, prioritizers, etc., to achieve this goal. Reference: https://academy.pega.com/module/one-one-customerengagement/topic/next-best-action-designer

Question 6

A telecom company is interested in improving customer engagement on social medi
a. However, there are hundreds of relevant messages posted every day, and it is not practical for
customer service representatives (CSRs) to review and respond to all messages. Instead, CSRs should
focus on negative messages. What do you need to analyze the incoming messages?

Accepted Answer

C

Explanation: A text categorization model is a type of text analytics model that can analyze the incoming messages and assign them to predefined categories, such as positive, negative, or neutral sentiment. This way, CSRs can focus on negative messages that require immediate attention or escalation. Reference: https://academy.pega.com/module/text-analytics/topic/creating-text-categorization-model

Question 7

Pega Decision Management enables organizations to make next-best-action decisions. To which types of decisions can next-best-action be applied?

Accepted Answer

D

Explanation: Pega Process AI™ lets you bring your own predictive models to Pega and use predictions in case types to optimize the way your application processes work and meet your business goals. To use the outcome of a predictive fraud model in the case type that processes the incoming claim, you need to use the model outcome in the condition of a decision step2. This way, you can route suspicious claims to a fraud expert for closer inspection based on the model’s prediction.

Question 8

Results of two simulations can be compared using the___________.

Accepted Answer

C

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

Question 9

U+ Insurance uses Pega Process AI™ to route complex claims to an expert. As a data scientist, you
have used the wizard to create a prediction with Case completion as the outcome to help with
decision routing. You are tasked with monitoring the adaptive models. When you open the
monitoring tab of the adaptive model rule, you see the following chart:
PEGACPDS88V1 practice exam questions

In this scenario, the system creates an adaptive model for each

Accepted Answer

B

Explanation: In this scenario, the system creates an adaptive model for each case type, such as claim or complaint. The adaptive model learns from the outcomes of each case type and predicts the probability of case completion for each customer. Reference: https://academy.pega.com/module/predicting-customerbehavior-using-real-time-data-archived/topic/adaptive-models-case-management

Question 10

The implementation of Next-Best-Action must involve

Accepted Answer

B

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

Question 11

What is the key difference between a predictive model and a human expert?

Accepted Answer

B

Explanation: :

Question 12

The result of a Predictive Model is stored in a property called__________.

Accepted Answer

D

Explanation: :

Question 13

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?

Accepted Answer

D

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.

Question 14

When building a predictive model, the Data Analysis stage is where you

Accepted Answer

B

Explanation: When building a predictive model, the Data Analysis stage is where you select the input data that will be used to train and test the model. You can also filter, group, or transform the input data to improve its quality and relevance. Reference: https://academy.pega.com/module/predictiveanalytics/topic/analyzing-data

Question 15

To predict if a customer is likely to churn you use a model of type

Accepted Answer

B

Explanation: To predict if a customer is likely to churn, you use a model of type decision tree. A decision tree is a type of predictive model that uses a set of rules to classify customers into different categories based on their attributes and behavior. A decision tree can predict a binary outcome (such as churn or not churn) or a multi-class outcome (such as low risk, medium risk, or high risk). Reference: https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models

Free Pegasystems PEGACPDS88V1 Actual Exam Questions