Pegasystems PEGACPDS88V1 Certified Pega Data Scientist 8.8 Online Training
Pegasystems PEGACPDS88V1 Online Training
The questions for PEGACPDS88V1 were last updated at Nov 19,2024.
- Exam Code: PEGACPDS88V1
- Exam Name: Certified Pega Data Scientist 8.8
- Certification Provider: Pegasystems
- Latest update: Nov 19,2024
A Scoring Model allows you to differentiate between
- A . Accept, Reject, Maybe Later
- B . Good, Bad
- C . Good, Better, Best
- D . Good, Bad, Unknown
C
Explanation:
A scoring model allows you to differentiate between Good, Better, and Best outcomes for a given proposition or action. A scoring model assigns a numerical value to each outcome based on its desirability or profitability for the business.
References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
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)
- A . The value of only the active predictors
- B . The outcome of the interaction
- C . The model metadata
- D . The propensity generated by the model
- E . The value of all predictors
B,E
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.
What is the difference between predictive and adaptive analytics?
- A . Predictive models can predict a continuous value.
- B . Predictive models predict customer behavior.
- C . Adaptive models use the customer data as predict*
- D . Predictive models have evidence.
C
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-models-overview
The outcome of a scoring model indicates the likely
- A . write-off value of an arrears case
- B . claim value of a health insurance
- C . period in which a spare part has to be replaced
- D . response to an offer
D
Explanation:
The outcome of a scoring model indicates the likely response to an offer that is presented to a customer. For example, a scoring model can predict if a customer will accept, reject, or defer an offer for a credit card upgrade.
References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
The standardized machine learning process (MLOps) lets you replace a low-performing predictive model that drives a prediction with an updated model. When you approve the model, a change request is automatically generated in__________
- A . the business operations environment
- B . an external environment
- C . the production environment
C
Explanation:
When you approve the updated model in the standardized machine learning process (MLOps), a change request is automatically generated in the production environment.
What is the key difference between a predictive model and a human expert?
- A . Predictive models always outperform human experts.
- B . Humans are better at dealing with structured data and identifying patterns.
- C . Predictive models are more capable of detecting patterns in historical data.
- D . Humans make successful predictions on a large amount of data.
As a data scientist, you are tasked with configuring two predictions that are driven by an adaptive model: one for an inbound channel and one for an outbound channel.
To which setting do you need to pay extra attention?
- A . Response timeout
- B . Adaptive model
- C . Predictor fields
- D . Control group
B
Explanation:
As a data scientist, if you are tasked with configuring two predictions that are driven by an adaptive model, you need to pay extra attention to adaptive model settings.
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?
- A . Case management_____
- B . Customer Decision Hub
- C . Text analytics
B
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.
Which component(s) do you use to calculate the average margin of four actions?
- A . one Set Property component
- B . one Group By component
- C . four Group By components
- D . four Set Property components
A
Explanation:
You can use one Set Property component to calculate the average margin of four actions by using an expression that sums up the margin values of each action and divides by four. You can then use this property in other components, such as Filter or Prioritize.
References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties
An adaptive adaptive model component in a decision: propensity, performance, evidence, and positives.
What is evidence in the context of an adaptive model?
- A . The likelihood of a statistically similar behavior
- B . The number of customers who exhibited statistically similar behavior
- C . The number of statistical bins that arc generated by the system
- D . The number of outcomes that system registered
B
Explanation:
Evidence is the number of customers who exhibited statistically similar behavior. It indicates how much data the model has collected for a given predictor profile. The higher the evidence, the more reliable the model is.
References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm
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