Get Pegasystems PEGACPDS88V1 Exam Practice Questions - Real and Updated

Answer : C

Predictive analytics is a branch of data science that uses statistical techniques and machine learning algorithms to find repeatable patterns in data and make predictions about future outcomes or behaviors. Reference: https://academy.pega.com/module/predictive-analytics/topic/predictive-analytics-overview

Answer : D

TheSwitchcomponent allows you to use a third-party Credit Risk Model 80% of the time and a Pega Credit Risk Model 20%.

Answer : C

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

Answer : B

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/predictive-analytics/topic/analyzing-data

Answer : C

A prediction helps in proactive retention by predicting the customer's churn risk. A prediction is an estimate of the likelihood of a future outcome based on historical data and statistical models. A prediction can help identify customers who are at risk of leaving and target them with appropriate actions to retain them. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning-/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm