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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| 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: Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Topic 4: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| 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:
Question 1
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
A. The volume of the data you collect
B. The location/source of your data collection
C. The makeup of your data team
D. The quality of the data you collect
Question 2
An AI system recommends loan approval decisions. Regulators require that a qualified employee make the final determination and be able to override the system.
What control does this requirement describe?
A. Model interpretability testing
B. Federated learning
C. Human-in-the-loop review
D. Automated bias auditing
Question 3
An insurance company wants to extract claim details from scanned handwritten forms and adjuster call recordings.
What characteristic of this data most affects the Data Preparation phase?
A. The data volume will require distributed storage
B. The data is unstructured and requires extraction and transformation before modeling
C. The data will need to be anonymized
D. The data is subject to retention requirements
Question 4
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. Use cross-validation techniques
C. Increase training epochs indefinitely
D. Increase model complexity
Question 5
Midway through an iteration, business stakeholders request that the model also predict two additional outcomes not included in the original scope.
What should the project manager do?
A. Reject the request as outside the approved scope
B. Evaluate the request against the iteration objective and defer it to a subsequent iteration if it does not fit
C. Extend the current iteration timeline to accommodate the additional outcomes
D. Incorporate the request to maintain stakeholder engagement
Solutions:
| Question 1 Answer: C | Question 2 Answer: C | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: B |



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