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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. Machine learning / generative AI model development
        • 2. Iterative delivery approach
          Topic 3: AI Operationalization and Governance- Deployment and lifecycle management
          • 1. AI governance and responsible AI
            • 2. Continuous improvement and monitoring
              Topic 4: Identify Business Needs and Solutions26%- Problem framing and business alignment
              • 1. Feasibility and ROI analysis
                • 2. Define AI business objectives
                  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 (Q208-Q213):

                          NEW QUESTION # 208
                          A logistics company is developing an AI system to manage and predict supply chain disruptions.
                          The project manager needs to evaluate the ethical implications of using this AI system. Which action will help to ensure ethical use?

                          Answer: C

                          Explanation:
                          Conducting an algorithmic impact assessment helps evaluate the ethical risks, potential harms, fairness concerns, transparency issues, and accountability requirements of the AI system before and during use in supply chain disruption management.


                          NEW QUESTION # 209
                          Your team is looking to develop an RPA bot to help with back-office processes such as data entry.
                          What type of bot should your team be creating?

                          Answer: A


                          NEW QUESTION # 210
                          A project manager is tasked with ensuring the transparency of an AI project's data selection process. The project involves integrating multiple data sources from various departments. What should the project manager do?

                          Answer: C

                          Explanation:
                          Creating a comprehensive data catalog documents available data sources, ownership, definitions, lineage, quality, and selection rationale. This provides transparency into why specific departmental data was chosen and how it will be used in the AI project.


                          NEW QUESTION # 211
                          You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique. What is this technique called?

                          Answer: A

                          Explanation:
                          Transfer Learning involves leveraging a model trained on one task (e.g., car recognition) and adapting it to a related task (e.g., animal recognition), significantly reducing development time and resources.


                          NEW QUESTION # 212
                          A project team is working on an AI solution that needs to be both scalable and compliant with various regulations. They need to ensure all cognitive components are thoroughly evaluated for ethical considerations. The team has identified multiple patterns for the project.
                          How should the team proceed?

                          Answer: B

                          Explanation:
                          A detailed ethical review ensures that every cognitive component is assessed for issues such as fairness, transparency, accountability, privacy, and regulatory implications. This allows the team to identify and address ethical risks before the AI solution is scaled or deployed.


                          NEW QUESTION # 213
                          ......

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