CPMAI Mock Exams & Practice CPMAI Tests

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PMI CPMAI Exam Syllabus Topics:

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

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                          CPMAI Practice Materials: Cognitive Project Management in AI (PMI-CPMAI) and CPMAI Study Guide - PracticeVCE

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q68-Q73):

                          NEW QUESTION # 68
                          A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality. Which method will meet the project team's objectives?

                          Answer: B


                          NEW QUESTION # 69
                          An aerospace engineering firm is developing a machine learning model to predict component failures. The project manager needs help to ensure the training data is representative of real- world scenarios. Which method will meet the project manager's objective?

                          Answer: C

                          Explanation:
                          PMI's CPMAI/PMI-CPMAI guidance emphasizes that, in the Data Understanding and Data Preparation phases, the team must identify appropriate datasets, evaluate training data requirements, validate "ground truth" quality, and explicitly assess data representativeness and potential bias issues before moving forward. Using historical data from multiple sources best supports representativeness because it increases coverage across operating conditions, environments, and failure modes that occur in real deployments (different fleets, sensors, maintenance practices, and duty cycles). This directly aligns with PMI's expectation that the project manager ensures readiness of data for model development through quality checks and representativeness assessments as part of go/no-go decisioning. In contrast, relying solely on synthetic data can reduce fidelity and distort real-world distributions if not carefully validated; competitor data often has ownership and fit-for-purpose limitations; and real-time monitoring is useful operationally but does not inherently make the training dataset representative. Therefore, aggregating and reconciling multi-source historical data is the most PMI-aligned method to meet the objective of representative training data prior to model development and evaluation.


                          NEW QUESTION # 70
                          You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?

                          Answer: C

                          Explanation:
                          K-Means clustering is a straightforward and widely used algorithm for grouping data into clusters, suitable for anomaly detection tasks that require classification into groups.


                          NEW QUESTION # 71
                          An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?

                          Answer: B

                          Explanation:
                          When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.


                          NEW QUESTION # 72
                          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: B

                          Explanation:
                          Analyzing the distribution of real-world data helps determine whether data drift has occurred. A shift between the data the model was trained on and the data it now receives can reduce accuracy and increase error rates, even when the model previously performed well.


                          NEW QUESTION # 73
                          ......

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