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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 11-08-2026 (see below)
A best practice in data science is to use a control group. What business metric is supported by this practice?
The lift that the prediction generates Reference:
Using a control group is a best practice in data science that supports the business metric ofthe lift that the prediction generates.
To predict if a customer is likely to churn you use a model of type
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). Reference: https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models
What are the most important aspects taken into consideration when determining the Next-Best-Action?
The most important aspects taken into consideration when determining the Next-Best-Action are business objectives and customer needs. Business objectives reflect the goals and priorities of the organization, such as increasing revenue, reducing costs, or managing risk. Customer needs reflect the preferences and expectations of the customers, such as their interests, intents, or life events. Reference: https://academy.pega.com/module/one-one-customer-engagement/topic/next-best-action-designer
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
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