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The Pega Certified Data Scientist (PCDS) 87V1 exam is a comprehensive certification that validates a candidate's skills and knowledge in data science. It is an ideal certification for data scientists, data analysts, and business analysts who want to enhance their skills and advance their careers in the field of data science. PEGAPCDS87V1 exam is challenging, but with proper preparation, candidates can pass the exam and earn the globally recognized Pega Certified Data Scientist (PCDS) certification.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2
The PEGAPCDS87V1 (Pega Certified Data Scientist (PCDS) 87V1) Certification Exam is designed to test the skills and knowledge of data scientists who use Pega's software to solve complex business problems. PEGAPCDS87V1 exam covers a wide range of topics, including data analysis, machine learning, predictive analytics, and data visualization. Passing PEGAPCDS87V1 exam demonstrates that a data scientist has the necessary skills to use Pega's software to analyze and interpret data, build predictive models, and make data-driven decisions.
The PEGAPCDS87V1 exam covers a wide range of topics related to data science and analytics, including data preparation, modeling, evaluation, and deployment. PEGAPCDS87V1 exam also tests the candidate's knowledge of Pega's software tools and how they can be used to solve real-world business problems. PEGAPCDS87V1 exam is designed to test both the theoretical knowledge of candidates and their practical skills in data science and analytics.
The PEGAPCDS87V1 exam is a computerized test that consists of 65 multiple-choice questions. The candidate has 90 minutes to complete the exam, and the passing score is 65%. PEGAPCDS87V1 exam is available in English and Japanese languages.
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| Section | Objectives |
|---|---|
| Data Preparation | - Data cleansing and transformation - Feature engineering for predictive models |
| Predictive Analytics | - Model training and evaluation - Predictive model types in Pega (classification, regression) |
| Data Sets and Integration | - External data integration with Pega platform - Data flows and dataset configuration |
| Pega Decisioning and AI Integration | - Adaptive models and decision strategies - Embedding predictive analytics in Pega applications |
| Data Understanding | - Exploratory data analysis concepts - Data sources and data types in Pega |
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