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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| CPMAI Methodology | 41% | - Phase IV: Model Evaluation
|
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning |
| Trustworthy AI | 9% | - Transparency and explainability - Ethical considerations and bias - Privacy and security |
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?
A. Implementing fairness-aware algorithms
B. Conducting bias audits during development
C. Using interpretable models for decision-making
D. Ensuring diverse training datasets
Question #2
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?
A. Careful algorithm selection that reduces the need for data.
B. Data Deduping to reduce overall size and data complexity.
C. Data integration focused on reducing the number of data sources.
D. Data selection and attribute pruning to reduce overall size and data complexity.
Question #3
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?
A. Use of inpainting to replace image components
B. Use of Generative Outpainting
C. Use of super-resolution to enhance the existing image
D. Prompt engineering for new image generation
Question #4
A data science team has produced working models through a series of ad hoc notebooks with hard-coded file paths and manually executed preprocessing steps.
What risk does this create for operationalization?
A. The models will not achieve sufficient accuracy
B. The data will not comply with retention policies
C. The team will be unable to explain model predictions
D. Accumulated technical debt prevents reliable, reproducible deployment
Question #5
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. Develop user-friendly interfaces which are tested by users.
B. Educate employees on new technologies so they can help users.
C. Standardize service protocols to deliver services for reliability.
D. Enhance data privacy to increase user trust and confidence.
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: D | Question #3 Correct Answer: B | Question #4 Correct Answer: D | Question #5 Correct Answer: D |


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