PMI-CPMAI™ FAQ — Common Questions (Prep, Scope, Format)

Do I need to be a data scientist?

No. You do need to understand how AI initiatives are delivered and governed: framing problems, data readiness, model validation, operationalization, monitoring, and responsible AI controls.

Is this mostly “AI theory”?

It’s primarily applied delivery decision-making. Expect scenario trade-offs involving privacy/security, feasibility, data access, QA/QC, governance, and operational readiness—not only definitions.

What should I study first?

Start with Overview, then follow a consistent loop:

1. read the Syllabus task set
2. do short drills in Practice
3. write a miss log
4. revisit weak objectives + do mixed sets

What are the highest-leverage topics?

• Responsible AI (privacy/security, transparency, bias checks, compliance)
• Problem framing, scope, success criteria, ROI
• Data definition, sourcing, access, evaluation, and communicating limits
• Model technique selection trade-offs, QA/QC, and go/no-go decisions
• Deployment plans, monitoring, governance, contingency, and transition

Where do official exam rules live?

Use Resources for official PMI links. For the primary source, see PMI’s certification page: https://www.pmi.org/certifications/ai-project-management-cpmai