PEGAPCDS87V1 Exam Dumps
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| Certification Provider: | Pegasystems |
|---|---|
| Exam Code / Number: | PEGAPCDS87V1 |
| Exam Name: | Pega Certified Data Scientist (PCDS) 87V1 |
| Exam Questions: | 50 |
| Last Updated: | Jun 22, 2026 |
| Corresponding Certification: | Pega PCDS |
(288 Up Votes)The PEGAPCDS87V1 Exam covers a broad range of topics that apply to data science methodologies and techniques. PEGAPCDS87V1 exam tests the student's ability to leverage Pega as a platform for conducting data analysis activities, with a focus on creating predictive models and generating accurate insights. It also includes tasks that involve data mining, statistical analysis, deep learning, and neural network technologies. The PEGAPCDS87V1 Exam aims to challenge and evaluate the student's ability to apply these techniques and use them in real-world scenarios. Passing the exam shows that the student understands how to deliver data-driven solutions for businesses through effective analysis and visualization techniques regardless of the complexity of the data.
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.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2
Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Adaptive Analytics | 28% | - Configuring and tuning predictors - Model performance monitoring and diagnostics - Adaptive Decision Manager (ADM) concepts |
| Pega Process AI | 10% | - Predictive case completion - Fraud and risk detection models |
| Predictive Analytics | 25% | - Importing and validating PMML/external models - Prediction Studio usage - Model evaluation metrics (AUC, lift, propensity) |
| Decision Strategies & Next-Best-Action | 20% | - Designing and testing decision strategies - Core decisioning principles - Arbitration and prioritization logic |
| Model Governance & Ethics | 5% | - Bias detection and compliance - AI governance framework |
| AI & Natural Language Processing | 12% | - Sentiment analysis and text processing - Pega NLP fundamentals |