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The PEGAPCDS86V1 exam covers a wide range of topics related to data science, including data preparation, feature engineering, modeling, and evaluation. Candidates are expected to have a solid understanding of statistical analysis, machine learning algorithms, and data visualization techniques. PEGAPCDS86V1 exam also covers the ethical considerations involved in data science and the ability to communicate data findings effectively to stakeholders. The Pega Certified Data Scientist certification is highly valued in the industry, as it demonstrates the candidate's proficiency in using data science to drive business decisions.

Reference: https://academy.pega.com/exam/pega-certified-data-scientist-1

The PEGAPCDS86V1 certification exam is one of the exams that Pegasystems offers as part of its PCDS program. PEGAPCDS86V1 exam is designed to evaluate a candidate's skills in using Pegasystems' products for data analytics and modeling. Successful completion of the exam indicates that the candidate has demonstrated a deep understanding of the theories, concepts, and practices of data science, as well as proficiency in working with Pegasystems' software for data analytics.

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Pegasystems PEGAPCDS86V1, also known as the Pega Certified Data Scientist (PCDS) 86V1 exam, is a certification exam offered by Pegasystems, a leading provider of business process management and customer engagement software solutions. PEGAPCDS86V1 exam is designed to test the knowledge and skills of data scientists who work with Pega's software solutions.

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Pegasystems PEGAPCDS86V1 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Model Deployment & Monitoring18%- Integrating models into decision strategies
- Performance tracking and retraining
- Governance and bias detection
Topic 2: Analytics & Business Application12%- Creating analytical reports and dashboards
- Translating insights to business outcomes
Topic 3: Data Preparation & Management22%- Handling missing values and imbalanced data
- Feature engineering and selection
- Data exploration and transformation
Topic 4: Pega Predictive Modeler28%- Importing PMML and external models
- Adaptive Decision Manager (ADM) configuration
- Building and training predictive models
Topic 5: Data Science Fundamentals20%- Predictive and adaptive analytics concepts
- Model evaluation metrics (AUC, lift, accuracy)
- Data requirements and statistical basics

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