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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
AI System Testing and Evaluation- Model evaluation and monitoring
  • 1. Performance evaluation and drift detection
    • 2. Explainability and reliability assessment
      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
              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
                      AI Operationalization and Governance- Deployment and lifecycle management
                      • 1. AI governance and responsible AI
                        • 2. Continuous improvement and monitoring

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q193-Q198):

                          NEW QUESTION # 193
                          An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?

                          Answer: A

                          Explanation:
                          Differences between training and production data distributions cause performance degradation.


                          NEW QUESTION # 194
                          A manufacturing company is using an AI system for quality control. The project manager needs to ensure data privacy and compliance with industry standards. Which initial approach will effectively address these requirements?

                          Answer: C

                          Explanation:
                          Developing a comprehensive data governance plan is the most effective initial approach because it defines how data is collected, stored, accessed, protected, retained, and monitored in alignment with privacy obligations and industry standards.


                          NEW QUESTION # 195
                          A supervised learning project requires 50,000 labeled images. The subject matter is highly specialized, and the organization has three qualified domain experts available part time.
                          What is the most significant project risk the manager should plan for?

                          Answer: A

                          Explanation:
                          Labeling is frequently the largest bottleneck in supervised learning projects, especially when it requires scarce domain expertise. The project manager must plan labeling capacity, cost, and duration explicitly rather than treating it as a minor preparation task.


                          NEW QUESTION # 196
                          Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

                          Answer: A

                          Explanation:
                          Descriptive analytics focuses on summarizing historical data to identify patterns and performance metrics, providing insights into past behaviors.


                          NEW QUESTION # 197
                          A project team at a healthcare provider is determining whether their patient records are adequate for an AI diagnostic tool. They need to validate that the data covers a broad spectrum of conditions and demographics. What is an effective method to assure data suitability?

                          Answer: B

                          Explanation:
                          Performing demographic analysis and stratifying patient data helps verify that the records adequately represent different patient groups and a broad range of conditions. This supports data suitability by ensuring the diagnostic AI model is trained and evaluated on representative healthcare data.


                          NEW QUESTION # 198
                          ......

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