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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 Jun. 10, 2025 (see below)
Prediction Studio supports keyword-based topic detection, model-based topic detection and the combination of these. When using machine learning,
When using machine learning, the Must keywords function aspositive features.
A company wants to create a data set that includes mock-up customer add which method is available to populate these fields?
The Monte Carlo method is a way to create a data set that includes mock-up customer data. You can use this method to generate random values for customer properties, such as age, gender, income, etc., based on predefined probabilities and distributions. Reference: https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set
When building a predictive model, at what stage do you compare the performance of predictive models?
When building a predictive model, you compare the performance of predictive models at the Model Comparison stage. This stage allows you to select the best model based on various metrics, such as accuracy, lift, or area under curve (AUC). Reference: https://academy.pega.com/module/predictive-analytics/topic/comparing-predictive-models
Results of two simulations can be compared using the___________.
TheProposition Distribution reportis used to compare the results of two simulations.
You can use various data types in adaptive analytics. Some of these require preprocessing before being used as a potential predictor. Others can be used directly. Which two data types require no preprocessing? (Choose Two)
Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income Reference:
Dates with absolute time/date values, such as birthdaysandNumeric data such as customer age and incomerequire no preprocessing before being used as potential predictors in adaptive analytics.
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