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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q51-Q56):

                          NEW QUESTION # 51
                          A project manager is defining the operationalization plan for a deployed model and must specify when retraining occurs.
                          What is the most effective approach?

                          Answer: D

                          Explanation:
                          Threshold-based triggers tie retraining to measured degradation rather than arbitrary intervals, avoiding both unnecessary retraining and prolonged periods of poor performance. Monitoring metrics against defined thresholds is a core operationalization control.


                          NEW QUESTION # 52
                          A government agency is operationalizing a new AI tool for predictive policing. The project manager needs to identify data subject matter experts (SMEs) to ensure data quality and relevance. The project team has access to historical crime data, socioeconomic data, and real- time incident reports. Which method will help in determining the data SMEs for this project?

                          Answer: D

                          Explanation:
                          Evaluating familiarity with historical crime and socioeconomic data helps identify the people who best understand the project's key datasets, their context, limitations, quality issues, and relevance to the predictive policing use case.


                          NEW QUESTION # 53
                          A company is using an AI model to screen job applications. Over time, the project team notices that the model disproportionately rejects applications from certain educational institutions. What is the first step the project manager should take?

                          Answer: B

                          Explanation:
                          Analyzing the historical data for inherent biases is the first step because the team must identify whether past hiring patterns, labels, or institutional representation caused the model to learn unfair rejection behavior. Understanding the source of bias is necessary before retraining, changing thresholds, or applying fairness controls.


                          NEW QUESTION # 54
                          Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?

                          Answer: B

                          Explanation:
                          Batch prediction suits scenarios needing periodic analysis, like weekly monitoring, by processing accumulated data in scheduled batches.


                          NEW QUESTION # 55
                          Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Choose all that apply.)

                          Answer: B,C,D,G

                          Explanation:
                          Effective AI governance requires robust system testing, continuous monitoring to detect issues, clear human accountability for decisions, and KPIs to measure system performance and impact.


                          NEW QUESTION # 56
                          ......

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