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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
AI Model Development and Iteration- Model building and validation
  • 1. Machine learning / generative AI model development
    • 2. Iterative delivery approach
      AI Operationalization and Governance- Deployment and lifecycle management
      • 1. AI governance and responsible AI
        • 2. Continuous improvement and monitoring
          Data Preparation for AI- Data cleaning and transformation
          • 1. Data quality assurance
            • 2. Feature engineering and preparation
              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 System Testing and Evaluation- Model evaluation and monitoring
                      • 1. Performance evaluation and drift detection
                        • 2. Explainability and reliability assessment

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q111-Q116):

                          NEW QUESTION # 111
                          You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
                          What is this technique called?

                          Answer: A


                          NEW QUESTION # 112
                          Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

                          Answer: D


                          NEW QUESTION # 113
                          Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm. What type of algorithm is the best approach for this project?

                          Answer: C

                          Explanation:
                          Clustering is an unsupervised learning method ideal for organizing data into groups based on similarities without predefined labels, making it suitable for data organization tasks.


                          NEW QUESTION # 114
                          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: A


                          NEW QUESTION # 115
                          A project team lacks sufficient labeled data for supervised learning but must still deliver a functional AI solution. Time constraints prevent extensive manual labeling. What is the most appropriate approach to continue development effectively?

                          Answer: D

                          Explanation:
                          Alternative learning approaches work with limited labeled data.


                          NEW QUESTION # 116
                          ......

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