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Pegasystems PEGACPDS88V1 Exam Questions
- Topic 1: AI for Customer Decision Hub: This PEGACPDS88V1 exam topic assesses the proficiency of data scientists in leveraging AI within the Customer Decision Hub. Data scientists will need to demonstrate their understanding of how to utilize predictions and insights to enhance decision-making processes.
- Topic 2: Adaptive Analytics: The knowledge of data scientists about Adaptive Analytics will be evaluated in this portion. It focuses on creating, monitoring, and exporting adaptive models. Expect questions on how to effectively implement and oversee these models to ensure accurate and actionable analytics for dynamic environments.
- Topic 3: Predictive Analytics: In this Pegasystems Certified Pega Data Scientist exam topic, data scientists will be tested on creating predictions, developing predictive models, and understanding MLOps practices. Their ability to harness predictive analytics to forecast outcomes and manage model lifecycles will be critical.
- Topic 4: Prediction Patterns: This PEGACPDS88V1 topic examines the skills of data scientists in creating and interpreting decision strategies and defining prediction patterns. They need to show their capability to develop and apply prediction patterns to optimize decision-making processes.
- Topic 5: Governance: Understanding the principles of governance within Pegasystems framework is the key in this topic. Aspiring data scientists will be tested on implementing and managing governance structures effectively.
- Topic 6: Pega NLP: This Pegasystems Certified Pega Data Scientist exam topic focuses on the ability of data scientists to use Pega Natural Language Processing tools. Data scientists will need to show expertise in text analytics for email routing and entity extraction with chatbots. This knowledge is essential for leveraging NLP to enhance customer interactions and automate processes.
- Topic 7: Pega Process AI: Here, the skills of data scientists in predicting fraud and case completion using Pega Process AI will be evaluated. Demonstrating the ability to apply AI for predicting these outcomes effectively will showcase the capacity of a data scientist to utilize AI for operational improvements and risk management.
Free Pegasystems PEGACPDS88V1 Exam Actual Questions
Note: Premium Questions for PEGACPDS88V1 were last updated On May. 22, 2026 (see below)
What is the key component of a Next-Best-Action strategy?
The key component of a Next-Best-Action strategy is a strategy, which is a graphical representation of the business logic that determines which actions to offer to each customer and in what order. A strategy can use various components, such as business rules, predictive models, filters, prioritizers, etc., to achieve this goal. Reference: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer
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_________.
When an adaptive model has not collected any evidence, its performance is 0.5, which means that it has no predictive power and is equivalent to a random guess. As more evidence is collected, the performance can increase or decrease depending on how well the model predicts customer behavior. Reference: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview
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 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 samplesincreases 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.
What happens when you increase the performance threshold setting of an adaptive model rule?
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. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm
Which adaptive model output is automatically mapped to a strategy property?
Propensity is the adaptive model output that is automatically mapped to a strategy property. Propensity is the likelihood of a positive response for a given action and predictor profile. It ranges from 0 to 100. Reference: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm
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