[Jun-2023] PEGACPDS88V1 Exam Dumps - Free Demo & 365 Day Updates

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The PEGACPDS88V1 certification exam covers a wide range of topics, including data analytics, predictive modeling, machine learning, and decisioning. Candidates are tested on their ability to analyze large data sets, build and deploy predictive models, and use Pega's decisioning capabilities to improve business outcomes. This certification exam is ideal for data scientists who want to enhance their skills and knowledge in implementing AI-powered solutions using Pega's platform. By earning this certification, candidates can differentiate themselves in the job market and open up new career opportunities in the field of AI and data science.

The PEGACPDS88V1 exam is a comprehensive exam that covers a wide range of topics related to data science. The exam evaluates the candidate's knowledge of data mining, machine learning, predictive analytics, and statistics, among other topics. The exam is designed to test the candidate's ability to apply these concepts to real-world scenarios and solve complex business problems.

 

NEW QUESTION # 85
For an Adaptive Model to react quickly to changes in customer behavior, the

• A. strategy must include the calculation for smooth propensity
• B. value of the memory setting should be set to a low number
• C. model must always evaluate all customer responses
• D. performance threshold should be set to a low number

Answer: D

NEW QUESTION # 86
To confirm the continuing accuracy of your adaptive models, adaptive models must be regularly inspected.
Which two tasks are part of a regular inspection? (Choose Two)

• A. Check the performance of individual predictors
• B. Check the performance and success rate of the models
• C. Update the models______________________________
• D. Adjust the advanced settings________________
• E. Add the historical data collected since the last inspection

Answer: A,B

Explanation:
Explanation
To confirm the continuing accuracy of your adaptive models, two tasks that are part of a regular inspection are check the performance and success rate of the models and check the performance of individual predictors.

NEW QUESTION # 87
The filter component is used to filter_______

• A. Adaptive models
• B. Actions
• C. attributes
• D. Customers

Answer: B

Explanation:
Explanation
The filter component is used to filter actions based on various criteria, such as eligibility, suitability, priority, or custom conditions. References:
https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/filtering-action

NEW QUESTION # 88
When building a predictive model, the Data Analysis stage is where you

• A. select the input data
• B. determine the output field
• C. create data samples
• D. group predictors

Answer: A

Explanation:
Explanation
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. References: https://academy.pega.com/module/predictive-analytics/topic/analyzing-data

NEW QUESTION # 89
A very important aspect of each model is how good a model or a given predictor is in predicting the required behavior. When building a predictive model, the use of testing and validation samples___________________

• A. enables model validation in strategies
• B. increases the accuracy of models
• C. validates the quality of input data
• D. is mandatory for segmentation

Answer: B

Explanation:
Explanation
A predictive model is a mathematical function that estimates the probability of an outcome based on input data. When building a predictive model, the use of testing and validation samples increases the accuracy of models123. Testing and validation samples are subsets of data that are used to evaluate how well a model performs on new data that was not used to train the model. Testing and validation samples help prevent overfitting, which is when a model learns too much from the training data and fails to generalize to new data.

NEW QUESTION # 90
Pega Adaptive Models_________

• A. involve a significant human effort to develop
• B. can only be used in inbound channels
• C. require historical data_________________
• D. learn about customer behavior in real time

Answer: D

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 # 91
When building a predictive model, what is a valid predictor data type?

• A. String
• B. Character
• C. Symbolic
• D. Boolean

Answer: D

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 # 92
When defining outcomes for an Adaptive Model you must define

• A. behavior values to be ignored
• B. only negative behavior values
• C. positive, negative and neutral behavior values
• D. one or more positive behavior values

Answer: D

Explanation:
Explanation
When defining outcomes for an adaptive model, you must define one or more positive behavior values, which indicate that the customer accepted or responded to the offer. You can also define negative and neutral behavior values, but they are optional. References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/configuring

NEW QUESTION # 93
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. Predictor fields
• B. Control group
• C. Response timeout
• D. Adaptive model

Answer: D

Explanation:
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.

NEW QUESTION # 94
What two tasks does a system architect need to perform to export historical data? (Choose Two)

• A. Export the data set
• B. Create a data set
• C. Validate the predictors used by the adaptive models
• D. Switch to a resilient repository
• E. Set the sample percentage for positive and negative outcomes

Answer: A,B

Explanation:
Explanation
Two tasks that a system architect needs to perform to export historical data are export the data set and create a data set.

NEW QUESTION # 95
Acquiring new customers can be more costly than retaining active customers. U+ Bank uses Pega Customer Decision Hub for its customer engagement and wants to reduce the churn rate by identifying high churn risk customers and making them a retention offer.
To meet this requirement, which two artifacts created by a data scientist allow the NBA specialist to implement the decision strategy? (Choose Two)

• A. A predictive model
• B. A control group
• C. An adaptive model
• D. A prediction

Answer: A,B

Explanation:
Explanation
According to the Data Scientist Student Guide1, page 18, the correct answer is B. A predictive model and C. A control group. A predictive model is a mathematical representation of a real-world process that can be used to predict an outcome based on input data. A control group is a subset of customers who are not exposed to a treatment (such as an offer) and are used to measure the effectiveness of the treatment by comparing their behavior with the treated group.

NEW QUESTION # 96
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. Propensity
• B. Context weighing
• C. Action value
• D. Business levers

Answer: A

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 # 97
Evidence an assessment of its viability, the Adaptive Model produces three outputs: Propensity, Performance and what is evidence in the context of an Adaptive Model? Performance and 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 customers who have responded to the modeled offer
• D. The number of statistical bins used to evaluate the response

Answer: B

Explanation:
Explanation
Evidence is the number of customers who exhibited statistically similar behavior to the current customer and responded to the modeled offer. It indicates how reliable the propensity score is based on the available data.
References:
https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-m

NEW QUESTION # 98
From two churn models with the similar performance, we chose the one the_____

• A. Fewest number of predictors
• B. Highest churn rate
• C. Most evidence
• D. Highest number of predictors

Answer: A

Explanation:
Explanation
The principle of parsimony states that from two models with similar performance, we choose the one with the fewest number of predictors. This is because a simpler model is easier to understand, maintain, and deploy.
References: https://academy.pega.com/module/predictive-analytics/topic/evaluating-predictive-models

NEW QUESTION # 99
Which decision component allows you to use a third-party Credit Risk Model 80% of the time and a Pega Credit Risk Model 20%?

• A. Filter
• B. Champion Challenger
• C. Adaptive Model
• D. Switch

Answer: D

Explanation:
Explanation
The Switch component allows you to use a third-party Credit Risk Model 80% of the time and a Pega Credit Risk Model 20%.

NEW QUESTION # 100
When selecting the list of predictors for an adaptive model you should

• A. Select at least one date property
• B. Consider properties from a wide range of sources
• C. Always use numeric type for integer properties
• D. Select up to a maximum of 500 predictors

Answer: B

Explanation:
Explanation
When selecting the list of predictors for an adaptive model you should consider properties from a wide range of sources. Predictors are properties that influence the customer behavior and can be derived from various sources such as customer profile, interaction history, proposition details, etc. References:
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision

NEW QUESTION # 101
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