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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Data for AI- Data identification and governance
  • 1. Data sourcing and selection
    • 2. Data compliance and ethics
      Identify Business Needs and Solutions26%- Problem framing and business alignment
      • 1. Feasibility and ROI analysis
        • 2. Define AI business objectives
          Data Preparation for AI- Data cleaning and transformation
          • 1. Feature engineering and preparation
            • 2. Data quality assurance
              AI System Testing and Evaluation- Model evaluation and monitoring
              • 1. Performance evaluation and drift detection
                • 2. Explainability and reliability assessment
                  AI Model Development and Iteration- Model building and validation
                  • 1. Iterative delivery approach
                    • 2. Machine learning / generative AI model development
                      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 (Q139-Q144):

                          NEW QUESTION # 139
                          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?

                          Answer: C

                          Explanation:
                          Conducting interviews to assess knowledge in anomaly detection helps identify SMEs who understand the specific data patterns, security signals, abnormal behaviors, and domain context needed to support an AI-powered security anomaly detection project.


                          NEW QUESTION # 140
                          Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled. Which phase of CPMAI is this done?

                          Answer: D

                          Explanation:
                          Phase II of CPMAI covers data preparation activities, including data labeling, to ensure the dataset is ready for model training.


                          NEW QUESTION # 141
                          You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

                          Answer: C


                          NEW QUESTION # 142
                          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 # 143
                          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?

                          Answer: B


                          NEW QUESTION # 144
                          ......

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