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The PEGAPCDS87V1 certification exam covers a wide range of topics including machine learning, data mining, statistical analysis, data visualization, and data modeling. PEGAPCDS87V1 exam is designed to test the candidate's ability to use Pega's tools to analyze data, build predictive models, and make data-driven decisions. It also tests the candidate's proficiency in using Pega's data science tools to solve complex business problems.
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The PEGAPCDS87V1 exam measures a candidate's knowledge of Pega’s data science tools, including Pega’s predictive analytics, machine learning, and data visualization modules. PEGAPCDS87V1 exam consists of multiple-choice questions that assess the candidate's ability to analyze business problems and design data-driven solutions using Pega’s tools. PEGAPCDS87V1 exam also evaluates the candidate's ability to interpret data, develop analytical models, and present insights to stakeholders. Passing the PEGAPCDS87V1 exam can help candidates demonstrate their proficiency in data science using Pega’s platform, which can help them gain recognition and advance their career as a data scientist.
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Pegasystems PEGAPCDS87V1 (Pega Certified Data Scientist (PCDS) 87V1) Certification Exam is an excellent opportunity for professionals to validate their skills and knowledge in the field of data science. Pega Certified Data Scientist (PCDS) 87V1 certification provides a solid foundation in data science and demonstrates the candidate's expertise in using Pega's platform for data analysis and predictive analytics. Pega Certified Data Scientist (PCDS) 87V1 certification also enhances the candidate's career prospects and demonstrates their commitment to their profession.
Reference: https://academy.pega.com/exam/pega-certified-data-scientist-2
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Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Adaptive Analytics | 28% | - Model performance monitoring and diagnostics - Adaptive Decision Manager (ADM) concepts - Configuring and tuning predictors |
| Topic 2: Decision Strategies & Next-Best-Action | 20% | - Designing and testing decision strategies - Arbitration and prioritization logic - Core decisioning principles |
| Topic 3: Model Governance & Ethics | 5% | - Bias detection and compliance - AI governance framework |
| Topic 4: Predictive Analytics | 25% | - Prediction Studio usage - Model evaluation metrics (AUC, lift, propensity) - Importing and validating PMML/external models |
| Topic 5: Pega Process AI | 10% | - Fraud and risk detection models - Predictive case completion |
| Topic 6: AI & Natural Language Processing | 12% | - Pega NLP fundamentals - Sentiment analysis and text processing |
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