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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q55-Q60):

                          NEW QUESTION # 55
                          A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?

                          Answer: C

                          Explanation:
                          PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one- time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI-CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation--foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes.


                          NEW QUESTION # 56
                          You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration.
                          What is the name for this process?

                          Answer: C


                          NEW QUESTION # 57
                          A manufacturing company is considering implementing an AI solution to optimize its supply chain.
                          The project manager needs to determine if AI is necessary for this task. Which action will address the requirements?

                          Answer: C

                          Explanation:
                          Identifying noncognitive versus AI methods helps determine whether the supply chain optimization task truly requires cognitive AI capabilities or can be solved with traditional automation, analytics, or rule-based methods. This ensures AI is used only where it adds meaningful value.


                          NEW QUESTION # 58
                          A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?

                          Answer: D

                          Explanation:
                          Creating a comprehensive data catalog documents available data sources, ownership, definitions, lineage, quality, and selection rationale. This provides transparency into why specific departmental data was chosen and how it will be used in the AI project.


                          NEW QUESTION # 59
                          You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?

                          Answer: A


                          NEW QUESTION # 60
                          ......

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