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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q296-Q301):

                          NEW QUESTION # 296
                          A project manager is evaluating the readiness of the team for an AI operationalization project.
                          They have identified a lack of experience with AI and data knowledge. If this issue is neglected, what is the outcome?

                          Answer: C

                          Explanation:
                          A lack of AI and data knowledge creates skill gaps that slow down planning, data preparation, model validation, deployment, and troubleshooting. If not addressed, the team will likely experience delays as they learn required concepts and practices during execution.


                          NEW QUESTION # 297
                          A telecommunications company is preparing data for an AI tool. The project team needs to ensure the data is in the right shape and format for model training. In addition, they are working with a mix of structured and unstructured data. Which method will address the project team's objectives?

                          Answer: D

                          Explanation:
                          Employing a data transformation tool helps standardize structured and unstructured data into consistent shapes, formats, and representations that can be used effectively for model training.


                          NEW QUESTION # 298
                          A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
                          What is an effective method to support an AI approach?

                          Answer: A

                          Explanation:
                          Conducting a cost-benefit analysis comparing AI and noncognitive solutions helps determine whether AI provides enough added value, such as better prediction, automation, scalability, or decision support, to justify its complexity, cost, and implementation effort.


                          NEW QUESTION # 299
                          Enhancing and cleaning data is an important action during which phase of CPMAI?

                          Answer: C

                          Explanation:
                          Phase II of CPMAI is focused on data preparation, including enhancing and cleaning data to ensure quality input for model training.


                          NEW QUESTION # 300
                          The team is evaluating where the sources of the data for training are. What phase of CPMAI are they in?

                          Answer: C


                          NEW QUESTION # 301
                          ......

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