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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q161-Q166):

                          NEW QUESTION # 161
                          A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

                          Answer: A

                          Explanation:
                          Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.


                          NEW QUESTION # 162
                          A project manager is evaluating an AI model's performance to determine its business impact. The model has shown high accuracy during testing but has not been implemented yet in a live environment. The project manager needs to decide what to assess to ensure the model's effectiveness. What should the project manager do?

                          Answer: A

                          Explanation:
                          Checking whether the training data represents real-world scenarios helps determine if the model's high testing accuracy is likely to translate into effective performance after deployment. A model can test well but fail to deliver business impact if the data used for training and testing does not reflect live operating conditions.


                          NEW QUESTION # 163
                          A model has passed evaluation and is approved for production. The project team has no defined process for versioning models, tracking which data version produced them, or rolling back a deployment.
                          What does this project lack?

                          Answer: C

                          Explanation:
                          Model versioning, data lineage, reproducibility, and rollback capability are core MLOps practices supporting the Model Operationalization phase. Without them, the organization cannot reliably reproduce, audit, or recover a deployed model.


                          NEW QUESTION # 164
                          A company is evaluating whether to implement AI for a project. They have defined their business objectives and determined the AI capability they want to use. Which action will enable the project manager to move forward with the project?

                          Answer: D

                          Explanation:
                          Conducting a go/no-go assessment confirms whether the AI project should proceed after the business objectives and desired AI capability have been defined. It evaluates readiness, feasibility, value, risks, and whether AI is the appropriate solution before committing further resources.


                          NEW QUESTION # 165
                          One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts. What course of action might make the issue worse?

                          Answer: D

                          Explanation:
                          Expanding the scope when facing data collection issues can worsen problems by increasing complexity and resource demands, making it harder to manage and deliver the project successfully.


                          NEW QUESTION # 166
                          ......

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