PMI CPMAI Exam Information and Actual Questions

  • Exam Code/Number: CPMAI
  • Exam Name/Title: Cognitive Project Management in AI (PMI-CPMAI)
  • Certification Provider: PMI
  • Corresponding Certification: CPMAI
  • Exam Questions: 272
  • Updated On: Aug 05, 2026

CPMAI
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PMI
CPMAI Exam
Cognitive Project Management in AI (PMI-CPMAI)

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PMI CPMAI Exam Overview:

Certification Vendor:Project Management Institute (PMI)
Exam Name:Cognitive Project Management in AI (CPMAI) / PMI-CPMAI Certification Exam
Exam Number:PMI-CPMAI
Real Exam Qty:120 (100 scored, 20 unscored)
Certificate Validity Period:3 years (renewal requires 30 PDUs)
Exam Duration:160 minutes
Available Languages:English
Exam Format:Multiple-choice (single best answer), Scenario-based questions
Recommended Training:PMI CPMAI Training and Exam Prep
Exam Registration:PMI Certification Portal
Sample Questions:PMI CPMAI Sample Questions
Exam Way:Online proctored exam or authorized testing center (Pearson VUE)
Pre Condition:No formal prerequisite; project management or AI/data experience recommended
Official Syllabus URL:https://www.pmi.org/

PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Data Preparation for AI- Data cleaning and transformation
  • 1. Data quality assurance
    • 2. Feature engineering and preparation
      Identify Business Needs and Solutions26%- Problem framing and business alignment
      • 1. Define AI business objectives
        • 2. Feasibility and ROI analysis
          Data for AI- Data identification and governance
          • 1. Data sourcing and selection
            • 2. Data compliance and ethics
              AI Model Development and Iteration- Model building and validation
              • 1. Machine learning / generative AI model development
                • 2. Iterative delivery approach
                  AI Operationalization and Governance- Deployment and lifecycle management
                  • 1. Continuous improvement and monitoring
                    • 2. AI governance and responsible AI
                      AI System Testing and Evaluation- Model evaluation and monitoring
                      • 1. Performance evaluation and drift detection
                        • 2. Explainability and reliability assessment


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