The PEGAPCDS86V1 exam covers a wide range of topics related to data science, including data preparation, feature engineering, modeling, evaluation, and deployment. It also covers topics such as statistical analysis, machine learning, and artificial intelligence. PEGAPCDS86V1 exam is designed to test an individual's ability to apply these concepts to real-world business problems and to use Pega's data science tools to develop solutions that meet business requirements.
The PEGAPCDS86V1 certification exam covers a wide range of topics, including data preparation, data exploration, feature selection, model evaluation, and deployment. PEGAPCDS86V1 exam also tests the candidate's understanding of different machine learning algorithms and their appropriate use in various business scenarios. Pega Certified Data Scientist (PCDS) 86V1 certification exam is ideal for data scientists who are looking to enhance their knowledge of Pega's predictive analytics capabilities and demonstrate their proficiency in using these capabilities to drive business value.
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Pegasystems PEGAPCDS86V1 (Pega Certified Data Scientist (PCDS) 86V1) Certification Exam is a highly sought-after certification for data scientists who specialize in Pegasystems' technology. Pega Certified Data Scientist (PCDS) 86V1 certification is designed to test the skills and knowledge of data scientists who work with Pegasystems' platform and applications. PEGAPCDS86V1 exam covers a wide range of topics, including data analysis, predictive modeling, and machine learning.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-1
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Pegasystems PEGAPCDS86V1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation & Management | 22% | - Feature engineering and selection - Data exploration and transformation - Handling missing values and imbalanced data |
| Topic 2: Pega Predictive Modeler | 28% | - Building and training predictive models - Adaptive Decision Manager (ADM) configuration - Importing PMML and external models |
| Topic 3: Data Science Fundamentals | 20% | - Model evaluation metrics (AUC, lift, accuracy) - Data requirements and statistical basics - Predictive and adaptive analytics concepts |
| Topic 4: Analytics & Business Application | 12% | - Creating analytical reports and dashboards - Translating insights to business outcomes |
| Topic 5: Model Deployment & Monitoring | 18% | - Performance tracking and retraining - Integrating models into decision strategies - Governance and bias detection |








