Exam Code : PEGACPDS88V1
Exam Name : Certified Pega Data Scientist 8.8
Vendor Name :
"Pegasystems"
PEGACPDS88V1 Dumps
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Certified Pega Data Scientist 8.8
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To enable an assessment of its reliability, the Adaptive Model produces three outputs: Propensity, Performance and Evidence. The performance of an Adaptive Model that has not collected any evidence is .
1-0
null C. 0.5
D. 0.0
Explanation:
The performance of an Adaptive Model that has not collected any evidence (i.e., hasn't been trained on any data yet) is typically indicated as null, as it doesn't have any basis for making accurate predictions yet.
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?
Create the prediction
Create the customer data model
Create a placeholder scorecard to drive the prediction
Create the predictive model that drives the prediction
Explanation:
To unblock the NBA specialist, as a data scientist, you should prioritize creating the predictive model that drives the prediction.
Which property is automatically recomputed for each decision component?
Property
Rank
Order
Priority
Explanation:
The rank property is automatically recomputed for each decision component. It indicates the order in which the actions are presented to the customer, based on their priority and propensity.
References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/ranking- actions
Pega Decision Management enables organizations to make next-best-action decisions. To which types of decisions can next-best-action be applied?
Determining how to optimize the product portfolio to increase market share
Determining why response rates for a campaign in one region are below average
Determining the cause of a customer's problem
Determining which banner to show on a web site to increase click rate
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 modelâs 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?
Pega Customer Decision Hub⢠prediction.
Sentiment detection does not require any predictions.
Case management prediction.
Text analytics prediction.
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
Calendar component
Filter component
Data Strategy property
Calendar strategy property
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 .
pyPrediction
pxResult
pyOutcome
pxSegment
Which value is output by an Adaptive Model?
Score
Performance
Behavior
Lift
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
Initiating contact to churn
A high value customer
In a collections process
Likely to churn
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 customerâs 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
An option
An optimized strategy
The number of customer's eligible for an action
The customer's propensity to accept an action
Explanation:
Adaptive model components can output the customerâs 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
case type instance
case type
case type step
case type stage
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?
The PMML standard is designed to facilitate the exchange of models between applications
The PMML standard can only be used to describe tree, scorecard and regression models.
The PMML standard is a proprietary standard
The PMML standard is designed to facilitate the exchange of scores between applications
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
uses the outcomes of the new model as predictors
is automatically replaced
drives the prediction
no longer drives the prediction
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