QUESTION: 13

Which data is usually not appropriate to be used as a predictor?

Customer zip code
Historical interaction data
Customer name
Usage data

Answer(s): C

Explanation:

Customer name is usually not appropriate to be used as a predictor. A predictor is a property that influences the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. Customer name is not likely to have any impact on the customer's preferences or responses, and it may also violate privacy regulations.

Reference:

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

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QUESTION: 14

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

Answer(s): A

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.

Reference:

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

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QUESTION: 15

The use of an imported third-party model in a decision strategy is____

Only possible after conversion into a Pega machine learning model
Identical to the use of an adaptive model
Similar to the use of a model built with Pega machine learning
Only possible after conversion into Pega markup language

Answer(s): 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.

Reference:

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

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QUESTION: 16

Proactive retention is applicable when a customer is

Initiating contact to churn
A high value customer
In a collections process
Likely to churn

Answer(s): D

Explanation:

Proactive retention is applicable when a customer is likely to churn. Proactive retention is a strategy that aims to prevent customer attrition by identifying customers who are at risk of leaving and offering them incentives or solutions to retain them. Proactive retention requires predicting the customer's churn risk and selecting the next best action accordingly.

Reference:

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

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Free PEGACPDS88V1 Exam Braindumps (page: 4)

QUESTION: 13

Explanation:

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QUESTION: 14

Explanation:

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QUESTION: 15

Explanation:

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QUESTION: 16

Explanation:

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