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PMI CPMAI Exam Syllabus Topics:

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

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

                          1. An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
                          Which set of metrics is most appropriate?

                          A) Response time only
                          B) Lines of code written
                          C) Number of features delivered
                          D) User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents


                          2. A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months. What's wrong with this timeframe?

                          A) It's not accounting for data preparation timelines
                          B) It fits into a waterfall timeframe, but not an agile project timeframe
                          C) It's underestimating the project timeline by 3 months
                          D) It's not accounting for potential project delays


                          3. A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains. Which method will help identify the qualified data SMEs?

                          A) Conducting interviews to assess their knowledge in anomaly detection
                          B) Assessing proficiency in developing generative adversarial networks (GANs) and experience in successfully generating synthetic data
                          C) Evaluating expertise with existing data architectures and their ability to optimize databases
                          D) Examining their expertise in neural network calibration and hyperparameter tuning


                          4. A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation. What is represented in this situation?

                          A) Compliance management
                          B) Transparency
                          C) Operational efficiency
                          D) Strategic alignment


                          5. A project team is currently evaluating an AI solution. They need to ensure the machine learning model provides the expected business benefits. Which critical factor should the project manager assess?

                          A) Maximization of model interpretability
                          B) Alignment with key performance indicators
                          C) Volume of training data
                          D) Minimization of human intervention


                          Solutions:

                          Question # 1
                          Answer: D
                          Question # 2
                          Answer: A
                          Question # 3
                          Answer: A
                          Question # 4
                          Answer: B
                          Question # 5
                          Answer: B

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