PMI-CPMAI™ tests applied AI project delivery: making sensible, responsible decisions under real constraints—privacy/security, governance, feasibility, data readiness, model validation, and operational reliability.
For the latest official exam details and requirements, see:
https://www.pmi.org/certifications/ai-project-management-cpmai
Official exam snapshot (PMI)
Source: PMI-CPMAI Examination Content Outline and Specifications — September 2025.
- Items: 120 total, including 20 unscored pretest questions
- Testing time: 160 minutes
- Breaks: none scheduled
- Tutorial + survey: optional (up to 15 minutes each), not counted against the 160-minute testing time
- Note: completion of the PMI-CPMAI exam prep course is required to sit the exam (per the exam content outline)
Official domain weights (PMI-CPMAI)
The Examination Content Outline specifies the proportion of questions by domain (the exact number may vary by form):
| Domain | Weight | Approx. target items (out of 120) |
|---|
| Support Responsible and Trustworthy AI Efforts | 15% | 18 |
| Identify Business Needs and Solutions | 26% | 31 |
| Identify Data Needs | 26% | 31 |
| Manage AI Model Development and Evaluation | 16% | 19 |
| Operationalize AI Solution | 17% | 21 |
What questions tend to reward
- Responsible delivery: privacy/security, transparency, bias checks, audit trails, and compliance monitoring.
- Framing and feasibility: the right problem, a realistic scope, and a defensible ROI/story.
- Data realism: data sources, access, SMEs, quality evaluation, and communicating data limits.
- Model governance thinking: technique selection trade-offs, QA/QC, training oversight, and go/no-go decisions.
- Operational discipline: deployment plans, monitoring, drift/updates, transition, contingency planning, and lessons learned.
Common pitfalls
- Jumping to model building before clarifying business need, constraints, and success criteria.
- Treating “data exists” as “data is usable” (privacy, access, quality, representativeness, lineage).
- Overlooking governance: transparency, bias checks, compliance monitoring, and auditability.
- Confusing offline accuracy with real-world value and operational reliability.
- Shipping without a plan to monitor, maintain, and respond to failures.
A practical prep loop
- Use the Syllabus as your coverage checklist.
- After each task set, review the matching part of the Cheatsheet and write a short “miss log.”
- Do focused drills in Practice, then re-drill the objectives behind every miss.
- Finish with mixed sets to force transfer across governance, framing, data, model, and operations scenarios.