CPMAI Review Guide - Latest CPMAI Exam Test

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
AI Operationalization and Governance- Deployment and lifecycle management
  • 1. Continuous improvement and monitoring
    • 2. AI governance and responsible AI
      Data for AI- Data identification and governance
      • 1. Data sourcing and selection
        • 2. Data compliance and ethics
          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. Machine learning / generative AI model development
                    • 2. Iterative delivery approach
                      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 (Q113-Q118):

                          NEW QUESTION # 113
                          Before fully deploying a new recommendation algorithm, the team wants to evaluate its performance against the existing system using real users. The goal is to measure impact on engagement and business metrics. What technique should be used?

                          Answer: B

                          Explanation:
                          A/B testing compares performance in real-world conditions.


                          NEW QUESTION # 114
                          A project team at an IT services company is developing an AI solution to enhance network security. They need to define the success criteria to help ensure the project achieves its desired outcomes. What should the project manager do to define the relevant success criteria?

                          Answer: B

                          Explanation:
                          Key performance indicators for incident response times and threat detection rates provide measurable success criteria tied directly to the network security objectives. They allow the project team to evaluate whether the AI solution improves detection effectiveness and response efficiency.


                          NEW QUESTION # 115
                          A project team has completed the Data Preparation phase. The prepared dataset meets quality thresholds, but the effort consumed 60 percent of the total project budget.
                          What should the project manager do at this stage?

                          Answer: B

                          Explanation:
                          Each CPMAI phase concludes with a go/no-go decision that reassesses whether continuing remains justified. A significant budget consumption changes the project's cost-benefit position, so the decision to proceed must be re-evaluated against the expected business value.


                          NEW QUESTION # 116
                          A model achieves 97 percent accuracy during validation but performs poorly in production.
                          Investigation reveals that a feature in the training set was derived from information that becomes available only after the outcome occurs.
                          What issue does this describe?

                          Answer: B

                          Explanation:
                          Data leakage occurs when the training data contains information that would not be available at prediction time. This inflates validation performance because the model is effectively learning from the answer, and the advantage disappears once the model is deployed.


                          NEW QUESTION # 117
                          A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects.
                          What's the next best course of action?

                          Answer: C


                          NEW QUESTION # 118
                          ......

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