Quiz PMI - CPMAI - Trustable Cognitive Project Management in AI (PMI-CPMAI) New Study Plan

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Operationalization and Governance- Deployment and lifecycle management
  • 1. Continuous improvement and monitoring
    • 2. AI governance and responsible AI
      Topic 2: Identify Business Needs and Solutions26%- Problem framing and business alignment
      • 1. Define AI business objectives
        • 2. Feasibility and ROI analysis
          Topic 3: AI System Testing and Evaluation- Model evaluation and monitoring
          • 1. Performance evaluation and drift detection
            • 2. Explainability and reliability assessment
              Topic 4: Data for AI- Data identification and governance
              • 1. Data compliance and ethics
                • 2. Data sourcing and selection
                  Topic 5: Data Preparation for AI- Data cleaning and transformation
                  • 1. Data quality assurance
                    • 2. Feature engineering and preparation
                      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 (Q197-Q202):

                          NEW QUESTION # 197
                          While developing a demand forecasting model, the team inadvertently includes future sales data during training. The model shows unusually high accuracy during testing but performs poorly in real deployment. What issue has occurred in this scenario?

                          Answer: D

                          Explanation:
                          Data leakage artificially inflates performance during training but leads to poor real-world outcomes.


                          NEW QUESTION # 198
                          Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

                          Answer: D


                          NEW QUESTION # 199
                          A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?

                          Answer: A

                          Explanation:
                          Improving data understanding and preparation is critical for optimizing representative training data because the team must assess data sources, quality, completeness, diversity, privacy constraints, and integration issues before training the AI system. This helps ensure patient data from multiple providers is usable, compliant, and representative across the hospital network.


                          NEW QUESTION # 200
                          Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it. What's going on here?

                          Answer: A

                          Explanation:
                          The "Uncanny Valley" effect describes discomfort people feel when robots or AI systems look almost - but not quite - human, causing them to appear creepy and reducing user acceptance.


                          NEW QUESTION # 201
                          A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?

                          Answer: D

                          Explanation:
                          Conducting bias audits during development directly supports an ethical risk assessment by systematically testing whether the AI system produces biased outcomes before it is deployed.
                          This helps identify, measure, and address bias risks early in the diagnostic workflow.


                          NEW QUESTION # 202
                          ......

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