Exam CPMAI Topics | CPMAI 100% Correct Answers

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PMI CPMAI Exam Syllabus Topics:

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

                          >> Exam CPMAI Topics <<

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q213-Q218):

                          NEW QUESTION # 213
                          During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?

                          Answer: D

                          Explanation:
                          Analyzing the distribution of real-world data helps determine whether data drift has occurred. A shift between the data the model was trained on and the data it now receives can reduce accuracy and increase error rates, even when the model previously performed well.


                          NEW QUESTION # 214
                          Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?

                          Answer: A


                          NEW QUESTION # 215
                          A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
                          If the team had provided insufficient context, what would be the result?

                          Answer: A

                          Explanation:
                          PMI guidance on prompts and prompt engineering states that prompts "supply the system with context and guidance as well as constraints," and that the value of a GenAI system "can only be realized through the instructions provided to it." PMI further explains that while an AI system can respond to very short inputs, "the less specific a prompt, the more likely the results will be vague or unhelpful," explicitly linking insufficient specificity/context to degraded usefulness. In PMI's recommended "diverge and converge" prompt approach, teams begin broad, then progressively refine, adding details such as industry, region, project type, intended use, and examples-- because "the granularity of the input will be directly proportional to the utility of the output received." Therefore, if the team provides insufficient context, the model must "guess" what is intended, which most directly manifests as answers that are not aligned to the actual task needs (i.e., lacking relevance), rather than being more accurate or more efficient.


                          NEW QUESTION # 216
                          An energy technology firm is developing an AI model to predict energy consumption patterns.
                          They need to gather the required data from smart meters, weather forecasts, and historical usage records. To help ensure the AI model performs well, which action will address their needs?

                          Answer: D

                          Explanation:
                          Integrating a data warehouse with real-time data ingestion supports combining smart meter readings, weather forecasts, and historical usage records into a reliable, timely, and structured data environment. This helps the model learn from both past patterns and current conditions to improve energy consumption predictions.


                          NEW QUESTION # 217
                          You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
                          Which phase best describes the work you are performing?

                          Answer: D


                          NEW QUESTION # 218
                          ......

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