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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - 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 |
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
| Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Your team is tasked with selecting an algorithm for a supervised learning classification project.
Which algorithm might you choose?
- A. K-means
- B. Gaussian mixture
- C. Q learning
- D. K-nearest neighbor
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A project team lacks sufficient labeled data for supervised learning but must still deliver a functional AI solution. Time constraints prevent extensive manual labeling. What is the most appropriate approach to continue development effectively?
- A. Use unsupervised or semi-supervised learning methods
- B. Cancel the project
- C. Duplicate existing labeled data
- D. Ignore labels and proceed
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An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?
- A. Response time only
- B. Number of features delivered
- C. Lines of code written
- D. User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
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You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?
- A. Retraining Pipelines
- B. Data Acquisition / Ingest / Capture
- C. ELT pipeline
- D. Feature Engineering
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A healthcare organization is evaluating a diagnostic screening model. Clinical stakeholders state that missing a positive case has far more serious consequences than a false alarm.
Which evaluation metric should be prioritized?
- A. Precision
- B. Overall accuracy
- C. Recall
- D. Specificity
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