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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Data for AI- Data identification and governance
  • 1. Data compliance and ethics
    • 2. Data sourcing and selection
      Data Preparation for AI- Data cleaning and transformation
      • 1. Feature engineering and preparation
        • 2. Data quality assurance
          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
                  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

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q235-Q240):

                          NEW QUESTION # 235
                          Your team is working on an AI system to provide a more personalized experience for customers on your website. What should the team do in regard to determining the pattern of AI with regards to the ROI of the project?

                          Answer: B


                          NEW QUESTION # 236
                          You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?

                          Answer: C


                          NEW QUESTION # 237
                          In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

                          Answer: B

                          Explanation:
                          Relying on users to label data can lead to inconsistent, low-quality labels and is generally inefficient, making it less beneficial compared to professional or curated labeling approaches.


                          NEW QUESTION # 238
                          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: C

                          Explanation:
                          AI is best suited for problems with probabilistic patterns where outcomes are uncertain and rules cannot be easily coded, making it a better fit than deterministic rule-based approaches.


                          NEW QUESTION # 239
                          When looking to implement AI to help break the Digital Transformation logjam, it's important to:

                          Answer: A


                          NEW QUESTION # 240
                          ......

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