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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
AI System Testing and Evaluation- Model evaluation and monitoring
  • 1. Performance evaluation and drift detection
    • 2. Explainability and reliability assessment
      Data Preparation for AI- Data cleaning and transformation
      • 1. Data quality assurance
        • 2. Feature engineering and preparation
          AI Operationalization and Governance- Deployment and lifecycle management
          • 1. AI governance and responsible AI
            • 2. Continuous improvement and monitoring
              Data for AI- Data identification and governance
              • 1. Data compliance and ethics
                • 2. Data sourcing and selection
                  Identify Business Needs and Solutions26%- Problem framing and business alignment
                  • 1. Define AI business objectives
                    • 2. Feasibility and ROI analysis
                      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 (Q286-Q291):

                          NEW QUESTION # 286
                          A manufacturing firm plans to use AI to predict equipment failures. The team can access sensor data but it contains many missing values and out-of-range readings. What should the project manager prioritize first?

                          Answer: B

                          Explanation:
                          PMI-CPMAI stresses that AI delivery is data-driven and iterative, and that teams must manage the Data Understanding work to identify appropriate datasets and validate quality before model development. Missing values and out-of-range readings can materially distort training and inference, so the PMI-aligned priority is to characterize the data: understand sources, sampling frequency, sensor health, definitions, and the nature of missingness (random vs. systematic), then define cleansing/imputation and anomaly-handling strategies as part of data preparation.


                          NEW QUESTION # 287
                          A project team is preparing to move to the next phase of implementation with an AI system.
                          However, they are unsure about the system's transparency requirements. Which next step should the project manager prioritize?

                          Answer: D

                          Explanation:
                          Reviewing existing transparency standards and guidelines should be prioritized because the team must first understand the applicable expectations, regulatory requirements, and accepted practices before defining documentation, reporting, or stakeholder processes for the next phase.


                          NEW QUESTION # 288
                          The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive. To keep your organization competitive, you need to:

                          Answer: C

                          Explanation:
                          Ensuring all senior leadership is data literate and understands big data concepts and their relevance to the organization is critical for strategic decision-making and maintaining a competitive advantage.


                          NEW QUESTION # 289
                          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: A

                          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 # 290
                          A financial services firm is assessing the success of a newly operationalized AI system for fraud detection. The project manager needs to evaluate the model against business key performance indicators. (KPIs). What is an effective method to help ensure the accuracy of this evaluation?

                          Answer: A

                          Explanation:
                          Utilizing a diverse set of validation techniques improves evaluation accuracy by checking the AI fraud detection system from multiple perspectives, such as model performance, business KPI alignment, false positive and false negative rates, and operational impact. This gives a more reliable assessment than relying on one metric or broad financial reporting alone.


                          NEW QUESTION # 291
                          ......

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