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The PEGAPCDS87V1 certification exam covers a wide range of topics related to data science and analytics, including data analysis, data visualization, machine learning, and predictive modeling. PEGAPCDS87V1 exam also tests candidates on their understanding of Pega software and how it can be used to support data science initiatives. PEGAPCDS87V1 exam consists of 60 multiple-choice questions, and candidates have two hours to complete it.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2
The PEGAPCDS87V1 exam covers a wide range of topics related to data science, including data preprocessing, data visualization, statistical analysis, machine learning, and model evaluation. PEGAPCDS87V1 exam consists of 60 multiple-choice questions that must be completed in 90 minutes. To pass the exam, candidates must score at least 65%. PEGAPCDS87V1 exam can be taken at a Pearson VUE testing center or online.
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Pegasystems PEGAPCDS87V1 (Pega Certified Data Scientist (PCDS) 87V1) Exam is a professional certification exam designed for individuals who want to demonstrate their expertise in using Pega's data science tools and techniques to solve complex business problems. PEGAPCDS87V1 exam covers a wide range of topics, including data exploration and visualization, predictive modeling, machine learning, and data analysis. PEGAPCDS87V1 exam is designed to test the candidate's knowledge and skills related to data science and analytics using Pega's technology stack.
Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:
| Section | Objectives |
| Data Sets and Integration | - Data flows and dataset configuration
- External data integration with Pega platform
|
| Pega Decisioning and AI Integration | - Adaptive models and decision strategies
- Embedding predictive analytics in Pega applications
|
| Data Preparation | - Data cleansing and transformation
- Feature engineering for predictive models
|
| Predictive Analytics | - Predictive model types in Pega (classification, regression)
- Model training and evaluation
|
| Data Understanding | - Data sources and data types in Pega
- Exploratory data analysis concepts
|