[Nov 25, 2023] New Real PEGACPDS88V1 Exam Dumps Questions
Pass Your PEGACPDS88V1 Exam Easily with Accurate Certified Pega Data Scientist 88V1 PDF Questions
The PEGACPDS88V1 exam is a comprehensive assessment of the candidate's knowledge and skills in Pega data science. PEGACPDS88V1 exam covers a wide range of topics, including data preparation, feature engineering, model selection, model evaluation, and deployment. Candidates are also tested on their understanding of Pega's built-in predictive analytics and machine learning tools, as well as their ability to integrate external data sources.
NEW QUESTION # 16
The P*C*V*L arbitration formula is used by the Customer Decision Hub to select the Next-Best-Action for each customer. Which factor in the arbitration formula is calculated using AI?
- A. Action value
- B. Business levers
- C. Context weighing
- D. Propensity
Answer: D
Explanation:
Explanation
Propensity Reference:
The PCV*L arbitration formula used by the Customer Decision Hub to select the Next-Best-Action for each customer calculates propensity using AI.
NEW QUESTION # 17
When building a predictive model, what is a valid predictor data type?
- A. String
- B. Symbolic
- C. Boolean
- D. Character
Answer: C
Explanation:
Explanation
When building a predictive model, a valid predictor data type is Boolean, which can have only two values:
true or false. Other valid predictor data types are numeric, date, and symbolic (categorical). References:
https://academy.pega.com/module/predictive-analytics/topic/predictor-data-types
NEW QUESTION # 18
The use of an imported third-party model in a decision strategy is____
- A. Only possible after conversion into Pega markup language
- B. Identical to the use of an adaptive model
- C. Only possible after conversion into a Pega machine learning model
- D. Similar to the use of a model built with Pega machine learning
Answer: D
Explanation:
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. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision
NEW QUESTION # 19
Which two factors do you inspect to assess the general health of the adaptive models in Prediction Studio?
(Choose Two)
- A. Number of decisions
- B. Performance of the models
- C. Model transparency
- D. Insights________________
Answer: A,B
Explanation:
Explanation
To assess the general health of the adaptive models in Prediction Studio, you can inspect the performance of the models and the number of decisions. The performance of the models shows how well they predict customer behavior over time. The number of decisions shows how much data is available for each model to learn from. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/monitoring
NEW QUESTION # 20
What happens when you increase the performance threshold setting of an adaptive model rule?
- A. The performance of the model is increased
- B. The number of active predictors may decrease
- C. The number of active predictors increases
- D. The correlation threshold decreases
Answer: B
Explanation:
Explanation
When you increase the performance threshold setting of an adaptive model rule, the number of active predictors may decrease. The performance threshold is the minimum performance that a predictor must have to be included in the model. If you increase this value, some predictors may not meet the criteria and be excluded from the model. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision
NEW QUESTION # 21
Which statement about predictive models is true?
- A. They need to be specified in a data attribute.
- B. You need past experience to create a predictive model.
- C. They are always associated with a proposition.
- D. They need unstructured big data.
Answer: B
NEW QUESTION # 22
Which property is automatically recomputed for each decision component?
- A. Rank
- B. Property
- C. Priority
- D. Order
Answer: A
Explanation:
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
NEW QUESTION # 23
Pega machine learning supports the creation of which two distinct types of predictive models? (Choose Two)
- A. Continuous
- B. Binary
- C. Categorical
- D. Numerical
Answer: B,C
Explanation:
Explanation
Pega machine learning supports the creation of two distinct types of predictive models: categorical and binary.
Categorical models predict the outcome of a variable that can have multiple values, such as product category or customer segment. Binary models predict the outcome of a variable that can have only two values, such as yes or no, accept or reject, etc. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision
NEW QUESTION # 24
Pega Adaptive Models_________
- A. require historical data_________________
- B. learn about customer behavior in real time
- C. involve a significant human effort to develop
- D. can only be used in inbound channels
Answer: B
Explanation:
Explanation
Pega adaptive models learn about customer behavior in real time by analyzing the responses to each offer and updating their predictions accordingly. They do not require historical data, human effort, or inbound channels to function. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-m
NEW QUESTION # 25
To predict if a customer is likely to churn you use a model of type
- A. decision tree
- B. switch
- C. champion challenger
- D. decision table
Answer: A
Explanation:
Explanation
To predict if a customer is likely to churn, you use a model of type decision tree. A decision tree is a type of predictive model that uses a set of rules to classify customers into different categories based on their attributes and behavior. A decision tree can predict a binary outcome (such as churn or not churn) or a multi-class outcome (such as low risk, medium risk, or high risk). References:
https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models
NEW QUESTION # 26
Predictions combine predictive analytics and best practices in data science. Which two best practices are included in Pega Customer Decision Hub predictions? (Choose Two)
- A. Setting the response timeout
- B. Selecting the model with the highest performance
- C. Using a control group
- D. Selecting the audience
Answer: B,C
Explanation:
Explanation
Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub predictions are using a control group and selecting the model with the highest performance.
NEW QUESTION # 27
When you create a decision strategy from scratch and want to associate an adaptive model with each action, you need to_______.
- A. Link all actions with an adaptive model decision component
- B. Define the adaptive model instances in Adaptive Decision Manager
- C. Capture the customer's response
- D. Create an adaptive model rule for each action
Answer: B
Explanation:
Explanation
To associate an adaptive model with each action in a decision strategy created from scratch, you must define the adaptive model instances in the Adaptive Decision Manager.
NEW QUESTION # 28
Adaptive model predictors are selected from the____________.
- A. communication channel
- B. proposition profile
- C. customer profile
- D. similar propositions
Answer: C
Explanation:
Explanation
Adaptive model predictors are selected from the customer profile, which contains information about the customer's attributes and behavior. Predictors can be either scalar or aggregate properties that capture customer context, such as channel, location, time, etc. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/configuring
NEW QUESTION # 29
In a decision strategy, the Adaptive Model decision component belongs the
- A. Business Rules category
- B. Predictive Model category
- C. Arbitration category
- D. Decision Analytics category
Answer: D
Explanation:
Explanation
In a decision strategy, the Adaptive Model decision component belongs to the Decision Analytics category.
This category contains components that use advanced analytics techniques, such as adaptive models, predictive models, text analytics models, etc., to make predictions or recommendations. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/decision-analyt
NEW QUESTION # 30
The likelihood that an action will be accepted by the customer is stored in the Strategy property called_______
- A. pyBehavior
- B. pyProbability
- C. pyLikelihood
- D. pyPropensity
Answer: D
Explanation:
Explanation
The pyPropensity property stores the likelihood that an action will be accepted by the customer. It is calculated by a predictive model or an adaptive model and used in decision strategies to prioritize actions. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/using-predictio
NEW QUESTION # 31
When developing a predictive model, the outcome value of a continuous model type can represent__________________
- A. the purchase value of an offer
- B. acceptance of an offer
- C. customer churn
- D. customer loan default
Answer: A
Explanation:
Explanation
When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.
NEW QUESTION # 32
In Prediction Studio, the key metrics of adaptive models are visualized in a bubble chart. What three key metrics are displayed in this chart? (Choose Three)
- A. Number of responses
- B. Number of active predictors
- C. Number of positive responses
- D. Success rate of the action
- E. Performance of the model
- F. Propensity of the model
Answer: A,E,F
Explanation:
Explanation
In Prediction Studio, the key metrics of adaptive models are visualized in a bubble chart. The three key metrics displayed in this chart are number of responses, propensity of the model, and performance of the model.
NEW QUESTION # 33
Adaptive models can start to learn without historical evidence. What is the starting propensity of every action?
- A. 0
- B. 1
- C. 2
- D. 0.5
Answer: D
Explanation:
Explanation
Adaptive models can start to learn without historical evidence. The starting propensity of every action is 0.5.
NEW QUESTION # 34
The Adaptive Model output that is automatically mapped to a strategy property is_________.
- A. propensity
- B. score
- C. evidence
- D. performance
Answer: A
Explanation:
Explanation
The adaptive model output that is automatically mapped to a strategy property is propensity, which indicates the likelihood that the customer will accept or respond to an offer. Propensity is also known as behavior or probability in decision strategies. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/using-adap
NEW QUESTION # 35
To enable an assessment of its reliability the adaptive model produces four outputs: propensity,performance, evidence and positives.
The Performance of an adaptive model that has not collected any evidence yet is______.
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
Explanation:
Explanation
The performance of an adaptive model that has not collected any evidence yet is 50. This means that the model is not confident about its predictions and assigns equal probability to all actions. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision
NEW QUESTION # 36
Configuring an adaptive model involves selecting the potential predictors. How many potential predictors are recommended for an adaptive model?
- A. Up to 100 fields to limit the impact on model speed
- B. At least 100 fields to reach an acceptable level of model performance
- C. All fields that have been predictive in the past
- D. All available uncorrected fields
Answer: A
Explanation:
Explanation
Up to 100 fields to limit the impact on model speed Reference:
When configuring an adaptive model, it is recommended to select up to 100 potential predictors to limit the impact on model speed.
NEW QUESTION # 37
Lift is key metric for the performance of the adaptive models.
To measure lift, you need a____________
- A. Control group with a new action
- B. Control group with random actions
- C. Standard model as a benchmark
- D. New adaptive model
Answer: B
Explanation:
Explanation
Lift is a key metric for the performance of the adaptive models. To measure lift, you need a control group with random actions. Lift is the ratio of the performance of the adaptive model to the performance of the control group. A control group is a subset of customers who receive random actions instead of the ones suggested by the model. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data-adm-/data-adm-model
NEW QUESTION # 38
For an Adaptive Model to react quickly to changes in customer behavior, the
- A. value of the memory setting should be set to a low number
- B. model must always evaluate all customer responses
- C. performance threshold should be set to a low number
- D. strategy must include the calculation for smooth propensity
Answer: C
NEW QUESTION # 39
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 stage
- B. case type instance
- C. case type step
- D. case type
Answer: D
Explanation:
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-m
NEW QUESTION # 40
In a predictive model rule, the predictors must be mapped to
- A. Customer properties
- B. Action properties
- C. Strategy properties
- D. Model properties
Answer: A
Explanation:
Explanation
Customer properties are used to map predictors to customer data that is available in the system. They can be either scalar or aggregate properties. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/mapping-predic
NEW QUESTION # 41
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Pegasystems PEGACPDS88V1 (Certified Pega Data Scientist 88V1) Exam is a certification exam that is designed to test the knowledge and skills of data scientists who work with Pega software. Pega software is a powerful tool that enables businesses to automate their processes and workflows, and data scientists who are skilled in using this software are highly sought after in today's job market.
PEGACPDS88V1 Certification Exam Dumps Questions in here: https://drive.google.com/open?id=1mhy_8ycf0-bnCNNJ4hWW_GrlZWmDuJZc
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