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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Model Development and Iteration- Model building and validation
  • 1. Iterative delivery approach
    • 2. Machine learning / generative AI model development
      Topic 2: Identify Business Needs and Solutions26%- Problem framing and business alignment
      • 1. Define AI business objectives
        • 2. Feasibility and ROI analysis
          Topic 3: AI System Testing and Evaluation- Model evaluation and monitoring
          • 1. Explainability and reliability assessment
            • 2. Performance evaluation and drift detection
              Topic 4: Data for AI- Data identification and governance
              • 1. Data compliance and ethics
                • 2. Data sourcing and selection
                  Topic 5: AI Operationalization and Governance- Deployment and lifecycle management
                  • 1. Continuous improvement and monitoring
                    • 2. AI governance and responsible AI
                      Topic 6: Data Preparation for AI- Data cleaning and transformation
                      • 1. Data quality assurance
                        • 2. Feature engineering and preparation

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q87-Q92):

                          NEW QUESTION # 87
                          A project charter states that the AI solution will "significantly improve customer experience." What is the deficiency in this statement?

                          Answer: D

                          Explanation:
                          Success criteria must be specific and measurable so that model evaluation can determine objectively whether the business objective is met. Qualitative statements provide no threshold against which results can be assessed at phase gates.


                          NEW QUESTION # 88
                          During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
                          When looking to gather data, what approach is best when determining how much data you need?

                          Answer: C


                          NEW QUESTION # 89
                          Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project. What type of algorithm would you select for this problem?

                          Answer: D

                          Explanation:
                          Spam filtering is a binary classification problem where emails are classified into one of two categories: spam or not spam.


                          NEW QUESTION # 90
                          Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?

                          Answer: A

                          Explanation:
                          Transparent AI focuses on providing visibility into how AI systems are built, including data sources and processes, to build user trust and understanding.


                          NEW QUESTION # 91
                          Clean, well-labeled datasets used for machine learning are partitioned into three subsets:
                          Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

                          Answer: C


                          NEW QUESTION # 92
                          ......

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