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

  • Exam Code: CPMAI
  • Exam Name: Cognitive Project Management in AI (PMI-CPMAI)
  • Updated: Jul 23, 2026
  • Q & A: 272 Questions and Answers

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Data for AI- Data identification and governance
  • 1. Data sourcing and selection
    • 2. Data compliance and ethics
      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. Continuous improvement and monitoring
            • 2. AI governance and responsible AI
              Identify Business Needs and Solutions26%- Problem framing and business alignment
              • 1. Feasibility and ROI analysis
                • 2. Define AI business objectives
                  Data Preparation for AI- Data cleaning and transformation
                  • 1. Feature engineering and preparation
                    • 2. Data quality assurance
                      AI System Testing and Evaluation- Model evaluation and monitoring
                      • 1. Performance evaluation and drift detection
                        • 2. Explainability and reliability assessment

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

                          1. One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts. What course of action might make the issue worse?

                          A) See if you already have access to enough data to continue with the project
                          B) See if you can expand the scope to continue with the project
                          C) See if you can adjust the scope of this interaction to continue with the project
                          D) See if you can purchase the data needed to continue with the project


                          2. A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains. Which method will help identify the qualified data SMEs?

                          A) Evaluating expertise with existing data architectures and their ability to optimize databases
                          B) Examining their expertise in neural network calibration and hyperparameter tuning
                          C) Conducting interviews to assess their knowledge in anomaly detection
                          D) Assessing proficiency in developing generative adversarial networks (GANs) and experience in successfully generating synthetic data


                          3. A project team is evaluating whether an AI initiative should proceed beyond discovery.
                          Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints. What is the most appropriate next action?

                          A) Move directly to deployment planning
                          B) Conduct a go/no-go assessment using readiness criteria
                          C) Begin model development using sample data
                          D) Purchase additional compute infrastructure


                          4. A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?

                          A) The prompts provide insufficient context and constraints
                          B) The tool requires more compute
                          C) The model is too efficient
                          D) The team is over-monitoring outputs


                          5. A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?

                          A) Running multiple end-user acceptance tests
                          B) Implementing an impact evaluation
                          C) Testing against validation datasets
                          D) Conducting a series of penetration tests


                          Solutions:

                          Question # 1
                          Answer: B
                          Question # 2
                          Answer: C
                          Question # 3
                          Answer: B
                          Question # 4
                          Answer: A
                          Question # 5
                          Answer: C

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