CPMAI exam preparatory: Cognitive Project Management in AI (PMI-CPMAI) & CPMAI actual lab questions

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q248-Q253):

                          NEW QUESTION # 248
                          An IT services company is working on a project to develop an AI-based customer support system.
                          During data preparation, the project manager needs to clean and transform customer interaction logs.
                          What is an effective technique to handle any missing data?

                          Answer: D


                          NEW QUESTION # 249
                          In a government project to develop an AI solution for optimizing public transportation schedules, it is crucial to determine the specific cognitive tasks that the AI will perform. The project manager needs to distinguish between tasks that require AI and those that do not.
                          Which approach should the project manager use to identify these tasks?

                          Answer: C

                          Explanation:
                          A detailed workflow analysis helps the project manager examine each process step, identify where cognitive capabilities such as prediction, reasoning, recognition, or decision-making are required, and separate those tasks from activities that can be handled through conventional automation or standard processes.


                          NEW QUESTION # 250
                          A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low. What is the cause for the precision issue?

                          Answer: B

                          Explanation:
                          Unbalanced training data can cause the model to favor the majority class and produce more false positives for the target outcome. This lowers precision because fewer of the model's positive predictions are actually correct.


                          NEW QUESTION # 251
                          In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:

                          Answer: A


                          NEW QUESTION # 252
                          In an IT services firm, the AI project team is tasked with developing a virtual assistant to support customer service operations. The assistant must integrate seamlessly with existing customer relationship management (CRM) systems and handle a variety of customer queries.
                          Which necessary initial task should the project manager take?

                          Answer: B

                          Explanation:
                          A comprehensive data audit should be performed first to identify what customer and CRM data is available, assess its quality and accessibility, and determine integration constraints. This establishes whether the virtual assistant has the reliable data foundation needed to handle customer queries effectively.


                          NEW QUESTION # 253
                          ......

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