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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Model Development and Iteration- Model building and validation
  • 1. Iterative delivery approach
    • 2. Machine learning / generative AI model development
      Topic 2: Data for AI- Data identification and governance
      • 1. Data compliance and ethics
        • 2. Data sourcing and selection
          Topic 3: AI Operationalization and Governance- Deployment and lifecycle management
          • 1. AI governance and responsible AI
            • 2. Continuous improvement and monitoring
              Topic 4: Identify Business Needs and Solutions26%- Problem framing and business alignment
              • 1. Feasibility and ROI analysis
                • 2. Define AI business objectives
                  Topic 5: Data Preparation for AI- Data cleaning and transformation
                  • 1. Feature engineering and preparation
                    • 2. Data quality assurance
                      Topic 6: AI System Testing and Evaluation- Model evaluation and monitoring
                      • 1. Performance evaluation and drift detection
                        • 2. Explainability and reliability assessment

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q240-Q245):

                          NEW QUESTION # 240
                          An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?

                          Answer: D

                          Explanation:
                          When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.


                          NEW QUESTION # 241
                          Which of the following best describes the technical definition of Machine Learning?

                          Answer: A


                          NEW QUESTION # 242
                          A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
                          What risk does this create for operationalization?

                          Answer: B

                          Explanation:
                          Manual, undocumented, and non-parameterized workflows cannot be reliably reproduced or automated in a production environment. This technical debt must be resolved through pipeline engineering before the model can be dependably operationalized.


                          NEW QUESTION # 243
                          A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation. What is represented in this situation?

                          Answer: A

                          Explanation:
                          Transparency is represented because the team is documenting AI decisions, rationale, impacts, and implementation issues so future reviewers can understand how and why choices were made.
                          This supports auditability, accountability, and traceability across the AI project.


                          NEW QUESTION # 244
                          An aerospace company is integrating AI for predictive maintenance. The project manager is concerned about potential delays due to external dependencies. Which initial step should the project manager take?

                          Answer: D

                          Explanation:
                          Establishing contingency plans is the appropriate initial step because external dependencies can create schedule risks outside the project team's direct control. Contingency planning helps define fallback actions, alternative timelines, escalation paths, and mitigation strategies before delays affect AI integration work.


                          NEW QUESTION # 245
                          ......

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