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The PEGACPDS88V1 certification exam is a comprehensive exam that covers various topics such as data preparation, predictive modeling, data analysis, data visualization, and machine learning using Pega's platform. Certified Pega Data Scientist 88V1 certification exam is designed to test the candidate's ability to apply Pega's platform to real-world scenarios, and to solve complex problems using predictive analytics techniques. PEGACPDS88V1 exam consists of multiple-choice questions and practical exercises that test the candidate's understanding and proficiency in Pega's predictive analytics platform.
Reference: https://academy.pega.com/exam/certified-pega-data-scientist-3
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The PEGACPDS88V1 exam consists of 65 multiple-choice questions that must be answered in 90 minutes. PEGACPDS88V1 exam is conducted online and can be taken from anywhere in the world. PEGACPDS88V1 exam questions are designed to test your knowledge and understanding of Pega software, as well as your ability to apply your knowledge to real-world scenarios. To pass the exam, you must score at least 65% or higher.
Pegasystems PEGACPDS88V1 certification exam is an excellent choice for data scientists who are seeking to advance their careers in the field of advanced analytics. Certified Pega Data Scientist 88V1 certification exam is designed to validate the skills and knowledge of data scientists who are experts in the Pega Platform. PEGACPDS88V1 exam is also a great choice for professionals who are seeking to enhance their skills in data science and machine learning using the Pega Platform. The Pegasystems PEGACPDS88V1 certification exam is a valuable credential that demonstrates the candidate's expertise in the field of data science and advanced analytics.
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: Pega NLP & Text Analytics | 12% | - Text analysis for routing and classification - NLP overview and use cases - Entity extraction and sentiment analysis |
| Topic 3: Predictive Analytics | 32% | - Building and importing predictive models - MLOps and model lifecycle management - Third-party model integration - Model validation and scoring |
| Topic 4: Decision Strategies & Prediction Patterns | 16% | - Defining reusable prediction patterns - Combining predictions in strategies - Designing decision logic - Arbitration and prioritization |
| Topic 5: Adaptive Analytics | 36% | - Exporting and interpreting model data - Monitoring and analyzing model performance - Adaptive model concepts and architecture - Model refresh and learning settings |







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