The Certified Pega Data Scientist 88V1 certification exam covers a wide range of topics related to data science, including data preparation, statistical analysis, predictive modeling, machine learning algorithms, and data visualization. Candidates will need to demonstrate their understanding of these topics by answering multiple-choice questions and completing practical exercises that require them to apply their knowledge in a simulated business environment. PEGACPDS88V1 exam is designed to be challenging, but it is also fair and objective, ensuring that only those who have mastered the material will pass.
Pegasystems PEGACPDS88V1 (Certified Pega Data Scientist 88V1) Exam is a certification exam that is designed to test the knowledge and skills of data scientists who work with Pega software. Pega software is a powerful tool that enables businesses to automate their processes and workflows, and data scientists who are skilled in using this software are highly sought after in today's job market.
Reference: https://academy.pega.com/exam/certified-pega-data-scientist-3
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Pegasystems PEGACPDS88V1 exam is a certification test designed for data scientists who want to prove their proficiency in Pega's data analytics tools. Passing PEGACPDS88V1 exam demonstrates that the candidate has the necessary skills and knowledge to work with Pega's predictive analytics and machine learning capabilities. With the rise of big data and the increasing demand for data scientists, this certification has become an essential credential for those looking to advance their career in the field.
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Pegasystems PEGACPDS88V1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance & Process AI | 4% | - Model governance and compliance - Ethics and fairness in AI models - Predicting SLA and fraud risks |
| Topic 2: Decision Strategies & Prediction Patterns | 16% | - Arbitration and prioritization - Combining predictions in strategies - Defining reusable prediction patterns - Designing decision logic |
| Topic 3: Pega NLP & Text Analytics | 12% | - NLP overview and use cases - Entity extraction and sentiment analysis - Text analysis for routing and classification |
| Topic 4: Adaptive Analytics | 36% | - Adaptive model concepts and architecture - Exporting and interpreting model data - Monitoring and analyzing model performance - Model refresh and learning settings |
| Topic 5: Predictive Analytics | 32% | - Building and importing predictive models - Third-party model integration - Model validation and scoring - MLOps and model lifecycle management |
