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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 9% | - Privacy and security - Ethical considerations and bias - Transparency and explainability |
| Topic 2: CPMAI Methodology | 41% | - Phase IV: Model Evaluation
|
| Topic 3: Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| Topic 4: Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) |
| Topic 5: AI Fundamentals | 16% | - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning |
| Topic 6: 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
The team is working to build a data preparation pipeline for the conversational chatbot project.
Which phase of CPMAI is this done?
A. Phase V
B. Phase III
C. Phase VI
D. Phase I
E. Phase IV
F. Phase II
Question 2
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?
A. The model would perform more efficiently.
B. The responses would lack relevance.
C. The model would generate more creative outputs.
D. The output would include higher accuracy.
Question 3
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. Standardize service protocols to deliver services for reliability.
C. Educate employees on new technologies so they can help users.
D. Develop user-friendly interfaces which are tested by users.
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 team will be unable to explain model predictions
C. The data will not comply with retention policies
D. Accumulated technical debt prevents reliable, reproducible deployment
Question 5
A project manager must report iteration progress to an executive sponsor who has no technical background. The team's most recent results include improvements in F1 score and AUC.
How should the progress be communicated?
A. Provide the technical report and offer to answer questions
B. Defer reporting until business impact can be measured in production
C. Present the metrics with definitions so the sponsor learns the terminology
D. Translate the metrics into their effect on the defined business success criteria
Solutions:
| Question 1 Answer: F | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: D |

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