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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: AI Model Development and Iteration- Model building and validation
      • 1. Machine learning / generative AI model development
        • 2. Iterative delivery approach
          Topic 3: Data Preparation for AI- Data cleaning and transformation
          • 1. Data quality assurance
            • 2. Feature engineering and preparation
              Topic 4: Identify Business Needs and Solutions26%- Problem framing and business alignment
              • 1. Define AI business objectives
                • 2. Feasibility and ROI analysis
                  Topic 5: AI Operationalization and Governance- Deployment and lifecycle management
                  • 1. Continuous improvement and monitoring
                    • 2. AI governance and responsible AI
                      Topic 6: 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 (Q245-Q250):

                          NEW QUESTION # 245
                          A project involves integrating AI systems across multiple departments, each with different access levels. This complex Al project has presented the project manager with significant issues related to data misuse. The project team has been focused on their ethics guidelines but continues to experience data misuse. The project involves different regional data protection regulations which further increases the complexity. What issue will cause these challenges to occur?

                          Answer: D

                          Explanation:
                          A detailed governance strategy is needed to define how data is accessed, used, shared, monitored, and controlled across departments and regions. Ethics guidelines alone are not enough when the project involves multiple access levels, potential misuse, and varying data protection regulations.


                          NEW QUESTION # 246
                          Midway through an iteration, business stakeholders request that the model also predict two additional outcomes not included in the original scope.
                          What should the project manager do?

                          Answer: D

                          Explanation:
                          Iterative delivery depends on protecting the current iteration's defined objective while capturing new requirements for future planning. Deferring the request preserves delivery cadence without discarding potentially valuable stakeholder input.


                          NEW QUESTION # 247
                          In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

                          Answer: D

                          Explanation:
                          Using Generative AI results to enhance and accelerate your work represents Augmented Intelligence, where AI assists and amplifies human capabilities without fully replacing human decision-making.


                          NEW QUESTION # 248
                          An IT services company is developing an AI system to automate network security monitoring. The project manager needs to consider various factors to mitigate risks associated with false positives and false negatives. Which action should the project manager implement?

                          Answer: C

                          Explanation:
                          Establishing a continuous feedback loop with security teams helps validate alerts, refine model behavior, and adjust detection rules or thresholds based on real-world monitoring outcomes. This directly reduces the impact of false positives and false negatives in network security operations.


                          NEW QUESTION # 249
                          A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
                          What risk does this create for operationalization?

                          Answer: A

                          Explanation:
                          Manual, undocumented, and non-parameterized workflows cannot be reliably reproduced or automated in a production environment. This technical debt must be resolved through pipeline engineering before the model can be dependably operationalized.


                          NEW QUESTION # 250
                          ......

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