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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI System Testing and Evaluation- Model evaluation and monitoring
  • 1. Explainability and reliability assessment
    • 2. Performance evaluation and drift detection
      Topic 2: Data Preparation for AI- Data cleaning and transformation
      • 1. Feature engineering and preparation
        • 2. Data quality assurance
          Topic 3: AI Operationalization and Governance- Deployment and lifecycle management
          • 1. Continuous improvement and monitoring
            • 2. AI governance and responsible AI
              Topic 4: AI Model Development and Iteration- Model building and validation
              • 1. Machine learning / generative AI model development
                • 2. Iterative delivery approach
                  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 (Q104-Q109):

                          NEW QUESTION # 104
                          A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
                          Which action should the project manager take?

                          Answer: C

                          Explanation:
                          An iterative approach allows the team to begin with the available data while revisiting earlier project activities as additional data becomes accessible. This supports progress without assuming that every data dependency must be resolved before the forecasting project can start.


                          NEW QUESTION # 105
                          Your team is tasked with selecting an algorithm for a supervised learning classification project.
                          Which algorithm might you choose?

                          Answer: C

                          Explanation:
                          K-nearest neighbor is a supervised learning algorithm used for classification tasks, making it suitable for supervised classification projects.


                          NEW QUESTION # 106
                          Your team is looking for a short term ROI project and decides that an AI enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition. What's the best course of action with how to proceed?

                          Answer: B

                          Explanation:
                          A clear problem definition is essential before moving forward; without it, the project risks misalignment with business goals and wasted resources, so progress should be paused until clarity is achieved.


                          NEW QUESTION # 107
                          You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?

                          Answer: C

                          Explanation:
                          While the data team makeup affects project execution, it has less direct impact on the specific data requirements compared to data quality, volume, and source considerations.


                          NEW QUESTION # 108
                          A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
                          However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?

                          Answer: C

                          Explanation:
                          Establishing continuous monitoring and feedback loops helps track model performance after deployment, detect drift or degradation, and support timely updates. This is essential for maintaining long-term reliability in real-world conditions.


                          NEW QUESTION # 109
                          ......

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