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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q276-Q281):

                          NEW QUESTION # 276
                          Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model. What critical error did your team make?

                          Answer: D

                          Explanation:
                          The critical error was not practicing model versioning and keeping all versions of the model.
                          Overwriting the original model without retaining previous versions prevents rollback to a known good model when performance issues arise. Proper versioning enables teams to track, compare, and revert to earlier models if needed.


                          NEW QUESTION # 277
                          A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?

                          Answer: C

                          Explanation:
                          Introducing data-centric methodology best practices directly addresses the core issues in AI projects and helps realign the team's approach for better outcomes.


                          NEW QUESTION # 278
                          You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)

                          Answer: A,B,C,D,F

                          Explanation:
                          A data scientist should have prompt engineering skills, expertise with data manipulation tools, strong critical thinking, solid understanding of algorithms, and strong math skills in calculus and statistics to effectively build and interpret AI models.


                          NEW QUESTION # 279
                          Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?

                          Answer: D

                          Explanation:
                          Transparent AI focuses on providing visibility into how AI systems are built, including data sources and processes, to build user trust and understanding.


                          NEW QUESTION # 280
                          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: B


                          NEW QUESTION # 281
                          ......

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