PMI CPMAI real exam prep : Cognitive Project Management in AI (PMI-CPMAI)

  • Exam Code: CPMAI
  • Exam Name: Cognitive Project Management in AI (PMI-CPMAI)
  • Updated: Oct 07, 2026
  • Q&As: 324 Questions and Answers

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify Business Needs and Solutions26%- Problem framing and business alignment
  • 1. Define AI business objectives
    • 2. Feasibility and ROI analysis
      Topic 2: AI Operationalization and Governance- Deployment and lifecycle management
      • 1. Continuous improvement and monitoring
        • 2. AI governance and responsible AI
          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 Model Development and Iteration- Model building and validation
                  • 1. Iterative delivery approach
                    • 2. Machine learning / generative AI model development
                      Topic 6: Data Preparation for AI- Data cleaning and transformation
                      • 1. Data quality assurance
                        • 2. Feature engineering and preparation

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

                          Question #1

                          A telecommunications company is preparing data for an AI tool. The project team needs to ensure the data is in the right shape and format for model training. In addition, they are working with a mix of structured and unstructured data. Which method will address the project team's objectives?

                          • A. Employing a data transformation tool to standardize formats
                          • B. Separating structured and unstructured data into different databases
                          • C. Using a hybrid storage system for both data types
                          • D. Converting unstructured data into structured formats
                          Reveal Solution  Discussion  0

                          Correct Answer: A  🗳️

                          Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                          Question #2

                          You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not. What is the best approach for this project?

                          • A. Pick a neural network algorithm since you know this works well for supervised learning approaches
                          • B. Pick an ensemble method since you're not sure which algorithm will perform best
                          • C. Pick a simple algorithm such a naive bayes
                          • D. Pick a simple algorithm such as Gaussian mixture
                          Reveal Solution  Discussion  0

                          Correct Answer: C  🗳️

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                          Question #3

                          A hospital system has been using a chatbot and has received complaints from end users. The end users believe they are speaking to a person but are frustrated when answers do not make sense. To help ensure end users know that they are engaging with an AI chatbot, what should be considered to support transparency?

                          • A. Disclosure notice with each use
                          • B. Inclusion of diverse data sets
                          • C. Use of interpretable AI models
                          • D. Operationalize advanced algorithms
                          Reveal Solution  Discussion  0

                          Correct Answer: A  🗳️

                          Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                          Question #4

                          During the business understanding phase, the project manager realizes that the project team has overlooked requirements for the AI model. The model should have a high level of explainability and transparency. What should the project manager do?

                          • A. Revisit the training data for the smart algorithm.
                          • B. Define a process for data aggregation and analysis.
                          • C. Develop an algorithm to account for future model adjustments.
                          • D. Ensure data compliance requirements are clearly documented.
                          Reveal Solution  Discussion  0

                          Correct Answer: D  🗳️

                          Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                          Question #5

                          You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

                          • A. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
                          • B. Store the data in a data warehouse
                          • C. Use noise reduction techniques to reduce all forms of data noise
                          • D. Implement a new data privacy policy
                          Reveal Solution  Discussion  0

                          Correct Answer: A  🗳️

                          Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

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