Last Updated: Jul 22, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| 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 |
1. A government agency plans to increase personalization of their AI public services platform. The agency is concerned that the personal information may be hacked. Which action should occur to achieve the agency's goals?
A) Enhance data privacy to increase user trust and confidence.
B) Develop user-friendly interfaces which are tested by users.
C) Educate employees on new technologies so they can help users.
D) Standardize service protocols to deliver services for reliability.
2. A project manager is considering different project management approaches for an AI solution deployment. They need to ensure the approach allows for iterative improvements and accommodates changing requirements. Which approach is effective in this situation?
A) Predictive
B) Incremental
C) Hybrid
D) Adaptive/agile
3. 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?
A) The "less is better" approach
B) The "goldilocks" approach
C) The "more is better" approach
D) There is no correct approach
4. A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data. If the project manager avoids addressing the variety of data during preparation, what will be the result?
A) Reduced model performance
B) Improved model accuracy
C) Decreased data processing speed
D) Increased data consistency
5. A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?
A) Building a unified data warehouse
B) Using an extraction, transformation, and loading (ETL) pipeline
C) Implementing a real-time data processing framework
D) Adopting a federated data model
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: C |
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