Pegasystems Pega Certified Data Scientist (PCDS) 87V1 : PEGAPCDS87V1

PEGAPCDS87V1 real exams

Exam Code: PEGAPCDS87V1

Exam Name: Pega Certified Data Scientist (PCDS) 87V1

Updated: Sep 02, 2026

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The Pega Certified Data Scientist (PCDS) certification is ideal for professionals who work with large amounts of data and want to advance their careers in data science. Pega Certified Data Scientist (PCDS) 87V1 certification demonstrates the candidate's ability to use Pega data science tools and techniques to solve complex business problems. Additionally, it validates the candidate's understanding of statistical concepts, machine learning algorithms, and predictive analytics. The PCDS certification is recognized globally and is highly respected in the industry, making it a valuable asset for any data scientist.

Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2

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The Pega Certified Data Scientist (PCDS) 87V1 Certification Exam is a valuable certification for professionals looking to enhance their skills and knowledge in the field of data science. PEGAPCDS87V1 exam is designed to test the proficiency of individuals in various aspects of data science, including machine learning, data analytics, data visualization, and data modeling. PEGAPCDS87V1 exam is targeted towards professionals who work with data on a daily basis, including data scientists, data analysts, business analysts, and IT professionals.

Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:

SectionObjectives
Topic 1: Predictive Analytics- Predictive model types in Pega (classification, regression)
- Model training and evaluation
Topic 2: Pega Decisioning and AI Integration- Adaptive models and decision strategies
- Embedding predictive analytics in Pega applications
Topic 3: Data Preparation- Data cleansing and transformation
- Feature engineering for predictive models
Topic 4: Data Sets and Integration- External data integration with Pega platform
- Data flows and dataset configuration
Topic 5: Data Understanding- Data sources and data types in Pega
- Exploratory data analysis concepts

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