Pegasystems PEGACPDS88V1 Certified Pega Data Scientist 8.8 Online Training
Pegasystems PEGACPDS88V1 Online Training
The questions for PEGACPDS88V1 were last updated at Nov 20,2024.
- Exam Code: PEGACPDS88V1
- Exam Name: Certified Pega Data Scientist 8.8
- Certification Provider: Pegasystems
- Latest update: Nov 20,2024
Pega Decision Management enables organizations to make next-best-action decisions.
To which types of decisions can next-best-action be applied?
- A . Determining how to optimize the product portfolio to increase market share
- B . Determining why response rates for a campaign in one region are below average
- C . Determining the cause of a customer’s problem
- D . Determining which banner to show on a web site to increase click rate
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.
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?
- A . Pega Customer Decision Hub™ prediction.
- B . Sentiment detection does not require any predictions.
- C . Case management prediction.
- D . Text analytics prediction.
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.
In a decision strategy, to remove propositions based on the current month, you use a
- A . Calendar component
- B . Filter component
- C . Data Strategy property
- D . Calendar strategy property
B
Explanation:
In a decision strategy, a filter component would be used to remove propositions based on specific criteria, such as the current month.
The result of a Predictive Model is stored in a property called__________.
- A . pyPrediction
- B . pxResult
- C . pyOutcome
- D . pxSegment
Which value is output by an Adaptive Model?
- A . Score
- B . Performance
- C . Behavior
- D . Lift
A
Explanation:
An Adaptive Model outputs a score, which is a quantified estimate of a certain behavior, such as the likelihood of a customer to accept an offer or the likelihood of a customer to churn
Proactive retention is applicable when a customer is
- A . Initiating contact to churn
- B . A high value customer
- C . In a collections process
- D . Likely to churn
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.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning-/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm
Adaptive model components can output__________
- A . An option___________
- B . An optimized strategy
- C . The number of customer’s eligible for an action
- D . The customer’s propensity to accept an action
D
Explanation:
Adaptive model components can output the customer’s propensity to accept an 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-adaptivemodel/main.htm
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:
In this scenario, the system creates an adaptive model for each
- A . case type instance
- B . case type
- C . case type step
- D . case type stage
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.
References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-case-management
Which statement about the PMML standard is correct?
- A . The PMML standard is designed to facilitate the exchange of models between applications
- B . The PMML standard can only be used to describe tree, scorecard and regression models.
- C . The PMML standard is a proprietary standard
- D . The PMML standard is designed to facilitate the exchange of scores between applications
A
Explanation:
The PMML standard is designed to facilitate the exchange of models between applications.
The standardized model operations process (MLOps) lets you replace a low-performing predictive model that drives a prediction with a superior one.
When you place the new model in shadow mode in the production environment, the current model___________
- A . uses the outcomes of the new model as predictors
- B . is automatically replaced
- C . drives the prediction
- D . no longer drives the prediction
C
Explanation:
When you place the new model in shadow mode in the production environment, the current model still drives the prediction, but the new model runs in parallel and collects performance data for comparison.
References: https://academy.pega.com/module/predictive-analytics/topic/mlops
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