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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data for AI- Data identification and governance
  • 1. Data sourcing and selection
    • 2. Data compliance and ethics
      Topic 2: Identify Business Needs and Solutions26%- Problem framing and business alignment
      • 1. Feasibility and ROI analysis
        • 2. Define AI business objectives
          Topic 3: AI System Testing and Evaluation- Model evaluation and monitoring
          • 1. Performance evaluation and drift detection
            • 2. Explainability and reliability assessment
              Topic 4: AI Model Development and Iteration- Model building and validation
              • 1. Iterative delivery approach
                • 2. Machine learning / generative AI model development
                  Topic 5: Data Preparation for AI- Data cleaning and transformation
                  • 1. Data quality assurance
                    • 2. Feature engineering and preparation
                      Topic 6: 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 (Q77-Q82):

                          NEW QUESTION # 77
                          An aerospace company is planning to integrate AI for predictive maintenance. The project team needs to identify which parts of the project require cognitive AI solutions. Which method should the project manager perform to meet the objective?

                          Answer: B

                          Explanation:
                          Conducting workshops with domain experts helps identify which predictive maintenance activities require cognitive AI capabilities, such as anomaly detection, failure prediction, and pattern recognition. Domain experts can map dependencies, clarify operational needs, and separate AI- suitable tasks from tasks that can be handled through traditional methods.


                          NEW QUESTION # 78
                          During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?

                          Answer: B


                          NEW QUESTION # 79
                          Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

                          Answer: A


                          NEW QUESTION # 80
                          An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?

                          Answer: D

                          Explanation:
                          Demonstrating the use of bias detection tools directly addresses ethical concerns by showing how the project team evaluates fairness, identifies potentially discriminatory outcomes, and supports responsible operationalization of the AI model.


                          NEW QUESTION # 81
                          Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project. What type of algorithm would you select for this problem?

                          Answer: C


                          NEW QUESTION # 82
                          ......

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