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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q315-Q320):

                          NEW QUESTION # 315
                          Clean, well-labeled, datasets used for machine learning are partitioned into three subsets:
                          Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

                          Answer: A

                          Explanation:
                          Random subsampling ensures that training, validation, and test sets are representative and unbiased samples of the overall data, which helps the model generalize well and provides reliable evaluation.


                          NEW QUESTION # 316
                          The project team at an IT services company is working on an AI-based customer support chatbot.
                          To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?

                          Answer: B

                          Explanation:
                          Gathering historical customer interaction logs provides real examples of customer questions, language patterns, issues, intents, and support responses. This helps define and supply the data needed to train an effective customer support chatbot.


                          NEW QUESTION # 317
                          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: B


                          NEW QUESTION # 318
                          A project manager is overseeing the development of an AI system and is concerned about maintaining user privacy. The project involves collecting sensitive user data with potential legal and compliance issues to consider.
                          What should the project manager prioritize to help ensure the AI system adheres to privacy requirements?

                          Answer: B

                          Explanation:
                          Regular privacy impact assessments help identify how sensitive data is collected, processed, stored, and used, while evaluating associated privacy risks against applicable legal and regulatory requirements. This enables the project team to maintain ongoing compliance throughout the AI system's lifecycle.


                          NEW QUESTION # 319
                          A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?

                          Answer: C

                          Explanation:
                          Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.


                          NEW QUESTION # 320
                          ......

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