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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
| Topic 2: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 3: Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| Topic 5: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Multiple teams are developing AI solutions independently, resulting in inconsistent ethical practices and governance standards. Leadership wants to ensure alignment across all projects.
What is the best action for the project manager to recommend?
- A. Allow teams full autonomy
- B. Increase model complexity
- C. Establish an AI governance framework and policies
- D. Focus only on technical performance
Correct Answer: C 🗳️
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A model has passed evaluation and is approved for production. The project team has no defined process for versioning models, tracking which data version produced them, or rolling back a deployment.
What does this project lack?
- A. A change control board
- B. A data governance framework
- C. An explainability assessment
- D. MLOps practices for model lifecycle management
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
An energy technology firm is developing an AI model to predict energy consumption patterns.
They need to gather the required data from smart meters, weather forecasts, and historical usage records. To help ensure the AI model performs well, which action will address their needs?
- A. Leveraging federated learning to keep data decentralized
- B. Integrating a data warehouse with real-time data ingestion
- C. Utilizing a data lake to store and process all data sources
- D. Adopting a hybrid cloud approach for scalable data management
Correct Answer: B 🗳️
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A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?
- A. Remove validation data
- B. Increase model complexity
- C. Increase training epochs indefinitely
- D. Use cross-validation techniques
Correct Answer: D 🗳️
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You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
- A. The location/source of your data collection
- B. The makeup of your data team
- C. The quality of the data you collect
- D. The volume of the data you collect
Correct Answer: B 🗳️
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