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The PEGAPCDS86V1 certification exam consists of 60 multiple-choice questions that must be completed within 90 minutes. PEGAPCDS86V1 exam covers various topics such as data preparation, statistical analysis, machine learning, predictive modeling, and data visualization. Candidates who successfully pass the exam will be awarded the Pega Certified Data Scientist (PCDS) 86V1 certification, which is valid for two years.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-1
The PEGAPCDS86V1: Pega Certified Data Scientist (PCDS) 86V1 exam is an important certification for data scientists who work with the Pega platform. It tests a range of skills and knowledge related to data science and predictive modeling, and can help data scientists stand out in a competitive job market. Pegasystems offers a range of training resources to help candidates prepare for the exam, and certified professionals receive a range of benefits, including access to a community of certified professionals and opportunities to showcase their skills to potential employers.
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The PEGAPCDS86V1 exam consists of 60 multiple-choice questions that must be answered within 90 minutes. PEGAPCDS86V1 exam covers a range of topics, including data analysis, model building and validation, decision strategy design, and deployment. Candidates must score at least 65% to pass the exam and earn the Pega Certified Data Scientist (PCDS) 86V1 certification.
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The Pega Certified Data Scientist (PCDS) 86V1 certification process includes a self-study training program, hands-on experience, and passing the PEGAPCDS86V1 Exam. Candidates who pass the exam will receive a Pega Certified Data Scientist (PCDS) 86V1 certification. Pega Certified Data Scientist (PCDS) 86V1 certification demonstrates that the data scientist has the necessary knowledge and skills to create, test and deploy predictive models within Pega CRM in real-world scenarios.
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Pegasystems PEGAPCDS86V1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Deployment & Monitoring | 18% | - Governance and bias detection - Performance tracking and retraining - Integrating models into decision strategies |
| Topic 2: Analytics & Business Application | 12% | - Creating analytical reports and dashboards - Translating insights to business outcomes |
| Topic 3: Pega Predictive Modeler | 28% | - Adaptive Decision Manager (ADM) configuration - Building and training predictive models - Importing PMML and external models |
| Topic 4: Data Science Fundamentals | 20% | - Model evaluation metrics (AUC, lift, accuracy) - Predictive and adaptive analytics concepts - Data requirements and statistical basics |
| Topic 5: Data Preparation & Management | 22% | - Feature engineering and selection - Handling missing values and imbalanced data - Data exploration and transformation |
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