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Last Updated: Jul 25, 2026
No. of Questions: 50 Questions & Answers with Testing Engine
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To earn the Pega Certified Data Scientist (PCDS) 87V1 certification, professionals must pass a challenging exam that tests their knowledge and skills in using Pegasystems software solutions. PEGAPCDS87V1 exam covers a wide range of topics, including data analysis, data modeling, machine learning, and predictive analytics. It is designed to ensure that professionals have the knowledge and skills they need to use Pegasystems software solutions effectively and make a real impact on their organizations.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2
The Pega Certified Data Scientist (PCDS) certification is ideal for professionals who work with large amounts of data and want to advance their careers in data science. Pega Certified Data Scientist (PCDS) 87V1 certification demonstrates the candidate's ability to use Pega data science tools and techniques to solve complex business problems. Additionally, it validates the candidate's understanding of statistical concepts, machine learning algorithms, and predictive analytics. The PCDS certification is recognized globally and is highly respected in the industry, making it a valuable asset for any data scientist.
The Pegasystems PEGAPCDS87V1 exam is structured in a way that tests the candidate's ability to apply their knowledge of data science to real-world scenarios. This means that candidates will be expected to demonstrate their ability to analyze data, build models, and extract insights from it.
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The PEGAPCDS87V1 exam covers a wide range of topics related to data science, including data preparation, feature engineering, model training and evaluation, and business analysis. PEGAPCDS87V1 exam consists of 60 multiple-choice questions that must be completed within 90 minutes. To pass the exam, candidates must score at least 70%.
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| Section | Weight | Objectives |
|---|---|---|
| Adaptive Analytics | 28% | - Adaptive Decision Manager (ADM) concepts - Configuring and tuning predictors - Model performance monitoring and diagnostics |
| Decision Strategies & Next-Best-Action | 20% | - Core decisioning principles - Designing and testing decision strategies - Arbitration and prioritization logic |
| Pega Process AI | 10% | - Fraud and risk detection models - Predictive case completion |
| Predictive Analytics | 25% | - Model evaluation metrics (AUC, lift, propensity) - Importing and validating PMML/external models - Prediction Studio usage |
| AI & Natural Language Processing | 12% | - Sentiment analysis and text processing - Pega NLP fundamentals |
| Model Governance & Ethics | 5% | - Bias detection and compliance - AI governance framework |
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