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PMI CPMAI Exam Syllabus Topics:

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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q270-Q275):

                          NEW QUESTION # 270
                          As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

                          Answer: D


                          NEW QUESTION # 271
                          Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?

                          Answer: C

                          Explanation:
                          Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.


                          NEW QUESTION # 272
                          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 # 273
                          In a government healthcare AI project, the objective is to reduce patient wait times by optimizing staff schedules. After 6 months, the cost is US$500,000 with a completion rate of 60%. The project manager needs to determine the return on investment (ROI) to justify the current expenditure. What is an effective method to achieve this objective?

                          Answer: A

                          Explanation:
                          PMI-CPMAI expects the project manager to determine ROI by calculating expected benefits, estimating total cost of ownership, developing a financially justified business case, and creating cost- benefit analysis to support stakeholder decisions. In this scenario, the project is only 60% complete, so the full benefits (reduced wait times, throughput gains, staffing efficiency) may not yet be fully realized or measurable. Under PMI's ROI determination intent--supporting business case justification while outcomes are still unfolding--an effective method is to project future benefits and compare them to investment, which is what an NPV model enables. NPV is useful when benefits accrue over time and when decision makers need a defensible view of value before full delivery, because it discounts future benefits and costs into today's terms for comparison.


                          NEW QUESTION # 274
                          An AI project team in the healthcare sector is tasked with developing a predictive model for patient readmissions. They need to gather required data from various sources, including electronic health records (EHR), patient surveys, and clinical notes. The team is evaluating which technique will help to ensure the data is comprehensive and reliable. What is an effective technique the project team should use?

                          Answer: B

                          Explanation:
                          Employing natural language processing helps extract relevant information from unstructured clinical notes, making the dataset more comprehensive and reliable for predicting patient readmissions. This ensures important patient context that is not stored in structured EHR fields or surveys can still be used effectively.


                          NEW QUESTION # 275
                          ......

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