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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q279-Q284):

                          NEW QUESTION # 279
                          You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with.
                          Which algorithm is the best choice given these constraints?

                          Answer: A


                          NEW QUESTION # 280
                          An energy technology company needs to convey data understanding to the leadership team for an AI project aimed at predicting equipment failures. The project involves analyzing sensor data from various equipment. Which necessary initial task should the project manager take?

                          Answer: D

                          Explanation:
                          Developing a preprocessing pipeline is the necessary initial task because sensor data must be cleaned, normalized, and prepared before it can be reliably analyzed, understood, or used to predict equipment failures.


                          NEW QUESTION # 281
                          An AI model is frequently retrained with new data to improve performance. However, stakeholders report inconsistent predictions across releases, causing confusion and reduced trust in the system. What risk is most likely occurring in this situation?

                          Answer: D

                          Explanation:
                          Frequent retraining without controls leads to inconsistent outputs and instability.


                          NEW QUESTION # 282
                          During which phase of an AI project should you consider Trustworthy AI considerations?

                          Answer: A


                          NEW QUESTION # 283
                          A project team plans to use customer records containing names, national identification numbers, and health information to train a model. The records were originally collected for billing purposes.
                          What is the primary concern the project manager must address?

                          Answer: A

                          Explanation:
                          Data protection frameworks restrict using personal data for purposes beyond those for which it was collected without a valid lawful basis. The project manager must confirm consent, legal basis, and applicable safeguards before the data enters the pipeline.


                          NEW QUESTION # 284
                          ......

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