A practical PMI-CPMAI™ study plan: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus by domain and tips for using the Mastery Cloud app.
This page answers the question most candidates actually have: “How do I structure my PMI-CPMAI™ prep?”
Pick a timeline, then follow the loop: Syllabus → drills → review misses → mixed sets.
Choose a plan based on hours per week:
| Time you can commit | Recommended plan | What it feels like |
|---|---|---|
| 12–18 hrs/week | 30‑day intensive | fast coverage + heavy practice |
| 7–10 hrs/week | 60‑day balanced | steady progress + room for review |
| 4–6 hrs/week | 90‑day part‑time | slower pace + repetition |
If you want one rule: start with ~60% learning + 40% practice, then flip to ~30% learning + 70% practice in the final 2 weeks.
PMI-CPMAI domain weights (ECO, Sep 2025):
| Domain | Weight | What to be good at |
|---|---|---|
| Responsible & trustworthy AI | 15% | privacy/security, transparency, bias checks, compliance, auditability |
| Business needs & solutions | 26% | problem framing, feasibility, scope, ROI, success criteria |
| Data needs | 26% | data definition, SMEs, sources, access/privacy, data evaluation and communication |
| Model development & evaluation | 16% | technique selection, QA/QC, training oversight, data prep, go/no-go decisions |
| Operationalize solution | 17% | deployment plans, monitoring, governance, transition, contingency, lessons learned |
Use the weights as your default time split, then adjust based on weak areas discovered in practice sets.
Target pace: ~12–18 hours/week.
Goal: cover the syllabus quickly, then harden decision instincts through drills and mixed sets.
| Week | Focus | What to do | Links |
|---|---|---|---|
| 1 | Business needs + feasibility | Work Domain II tasks; drill immediately after each task set; write a miss log. | Syllabus • Practice |
| 2 | Data needs | Work Domain III; practice data access/quality/evaluation scenarios; keep your “data risks” checklist. | Syllabus • Cheatsheet |
| 3 | Responsible AI + model development | Domain I + Domain IV; focus on governance, bias checks, QA/QC, and go/no-go gates. | Cheatsheet • Practice |
| 4 | Operationalize + final review | Domain V; then mixed sets and miss-log cleanup. | Practice • FAQ |
Target pace: ~7–10 hours/week.
Goal: cover each domain with reinforcement and spaced repetition.
| Weeks | Focus | What to do |
|---|---|---|
| 1–2 | Business needs & solutions | Clarify objectives, scope, ROI, success criteria; drill after each task set. |
| 3–4 | Data needs | Define required data, find SMEs, map sources, validate access/privacy, and evaluate fit. |
| 5 | Responsible & trustworthy AI | Transparency, bias checks, compliance monitoring, audit trails, security planning. |
| 6 | Model development & evaluation | Technique selection, QA/QC, training oversight, data prep, go/no-go decisions. |
| 7–8 | Operationalize + consolidation | Deployment, monitoring, governance, transition, contingency, lessons learned; then mixed sets. |
Target pace: ~4–6 hours/week.
Goal: slow-and-solid coverage with repetition and steady practice.
| Month | Focus | What to do |
|---|---|---|
| 1 | Business framing + data basics | Domains II–III foundation, light drills 3–4×/week. |
| 2 | Responsible AI + model work | Domains I + IV, add checklists and go/no-go patterns; drill after each task. |
| 3 | Operations + review | Domain V coverage, then mixed sets and miss-log cleanup. |