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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q272-Q277):

                          NEW QUESTION # 272
                          An IT services company project manager is creating an AI project scope statement. They need to include details on the environments, devices, and personnel that will use the AI solution. What should the project manager do?

                          Answer: C

                          Explanation:
                          Developing a comprehensive usage scenario analysis defines how the AI solution will be used, including the operating environments, devices, user roles, workflows, and personnel involved.
                          This provides the practical context needed for a complete AI project scope statement.


                          NEW QUESTION # 273
                          You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy. What is your next best course of action to address this?

                          Answer: C

                          Explanation:
                          Sharing your prior experiences with how your previous team approached AI projects helps educate senior management on the complexities of AI implementation and the importance of doing AI rather than just buying it.


                          NEW QUESTION # 274
                          A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data. If the project manager avoids addressing the variety of data during preparation, what will be the result?

                          Answer: C

                          Explanation:
                          Failing to address the variety of structured and unstructured data can leave the model with inconsistent formats, incomplete representations, and unusable inputs. This reduces the model's ability to learn accurate fleet management patterns and leads to weaker performance.


                          NEW QUESTION # 275
                          A team preparing data for a churn model transforms raw timestamps into derived attributes such as days since last purchase and purchase frequency over 90 days.
                          What activity is being performed?

                          Answer: D

                          Explanation:
                          Feature engineering creates new input variables from raw data to expose relationships that models can learn more effectively. Derived behavioral attributes often provide substantially more predictive signal than the underlying raw timestamps.


                          NEW QUESTION # 276
                          An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?

                          Answer: A

                          Explanation:
                          Understanding the data characteristics is the first step because the project manager must determine how the data is structured, sourced, aggregated, formatted, and related before identifying why inaccuracies may occur. This provides the foundation for evaluating freshness, relevance, cleansing needs, and aggregation risks.


                          NEW QUESTION # 277
                          ......

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