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Most Recent Pegasystems PEGACPDS88V1 Exam Dumps
Prepare for the Pegasystems Certified Pega Data Scientist 8.8 exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.
QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Pegasystems PEGACPDS88V1 exam and achieve success.
The questions for PEGACPDS88V1 were last updated on May 4, 2025.
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The outcome of a scoring model indicates the likely
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. Reference: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models
A legal firm wants to use text analytics for easier and faster access to information to helo with compliance related issues. The legal firm needs a taxonomy of legal concepts.
What is a taxonomy?
A taxonomy is a list of valid categories that can be used to classify text documents or entities. A taxonomy can be hierarchical or flat, depending on the level of detail required. Reference: https://academy.pega.com/module/text-analytics/topic/creating-taxonomy
.Prediction Studio supports keyword-based topic detection, model-based topic detection, or a combination of both. When using a text prediction based on machine learning with keywords configured,_________________.
When using a text prediction based on machine learning with keywords configured, the Not keywords function as negative features, meaning that they reduce the probability of detecting the topic if they appear in the text. The Must keywords and May keywords do not have any impact on the machine learning model. Reference: https://academy.pega.com/module/text-analytics/topic/configuring-keywords
A large online store uses Pega Customer Decision Hub to smoothly adapt to changing customer behavior. Adaptive models help accomplish this business objective as the models learn from customer responses.
Which statement about adaptive models is correct? s
Adaptive models perform a binary model calculation. This means that adaptive models predict the likelihood of a positive or negative response for each action and customer profile. Adaptive models do not require underlying predictive models or historical data sets to start learning. They learn from customer responses in real time and continuously update their predictions. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm
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)
According to theData Scientist Student Guide1, page 18, the correct answer isB. A predictive modelandC. 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.
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