Exam Code: PEGAPCDS80V1_2019
Exam Name: Pega Certified Data Scientist (PCDS) 80V1 2019
Updated: Sep 10, 2026
Q & A: 0 Questions and Answers
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The Pega Certified Data Scientist (PCDS) 80V1 2019 Certification Exam covers a wide range of topics, including data preparation, data modeling, predictive analytics, decisioning, and machine learning. Candidates will need to have a solid understanding of data science principles, as well as experience working with Pega's tools, in order to pass the exam.
Pegasystems developed the certification exam to meet the increasing demand for talented data scientists with advanced skills in using Pega's technology. Pega Certified Data Scientist (PCDS) 80V1 2019 certification serves as a valuable addition to the resumes of candidates who possess strong analytical and technical skills and are looking for career advancement opportunities in the field of data science.
To prepare for the Pegasystems PEGAPCDS80V1_2019 certification exam, candidates are encouraged to take Pega's data science courses and complete the certification exam practice tests. These courses are designed to provide hands-on experience with Pega's data science tools and help candidates prepare for the certification exam. Additionally, candidates can also benefit from studying data science concepts and theories from other sources to gain a deeper understanding of the field.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Text and Predictive Analytics Tools | 10% | - Text analytics configuration - Predictive analyzer usage |
| Topic 2: Decision Strategy Design | 25% | - Decision strategy canvas - Simulation and validation - Eligibility and arbitration logic |
| Topic 3: Adaptive Model Management | 25% | - Adaptive model configuration - Model performance monitoring - Model refresh and retraining |
| Topic 4: Pega Customer Decision Hub Implementation | 20% | - Real-time decisioning - Integration with predictive models - Outcome analysis and reporting |
| Topic 5: Predictive Analytics Fundamentals | 20% | - Statistical modeling concepts - Predictive model types - Data preparation and feature engineering |
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