PMI PMI-CPMAI Exam Details & Actual Exam Questions
- Exam Code/Number: PMI-CPMAI
- Exam Name/Title: PMI Certified Professional in Managing AI
- Certification Provider: PMI
- Corresponding Certification: CPMAI
- Exam Questions: 141
- Updated On: Jul,20 2026
- Certification Level: Specialized / Professional
PMI Certified Professional in Managing AI Exam Questions
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(33 Up Votes)PMI PMI-CPMAI Exam Overview:
| Certification Vendor: | PMI (Project Management Institute) |
| Exam Name: | PMI Certified Professional in Managing AI |
| Exam Number: | PMI-CPMAI |
| Related Certifications: | PMP (Project Management Professional) PMI-ACP (Agile Certified Practitioner) PMI-PBA (Professional in Business Analysis) |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple Choice |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD $520 for PMI members / USD $670 for non-members |
| Exam Duration: | 150 minutes |
| Real Exam Qty: | 120 |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Exam Way: | Computer-based testing at PMI-authorized Pearson VUE test centers worldwide |
| Pre Condition: | No mandatory prerequisites. However, basic project management knowledge (PMP or equivalent experience) is recommended. Secondary school diploma required if pursuing PMI membership. |
| Official Syllabus URL: | https://www.pmi.org/certifications/certified-professional-managing-ai-cp-ai |
PMI PMI-CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Project Lifecycle | 25% | - AI deployment and monitoring - Model testing and validation - Data acquisition and preparation - Model development and training - Iterative and agile approaches for AI - AI project planning and scoping |
| AI Fundamentals and Context | 15% | - AI history and evolution - AI technologies and techniques overview - Types of AI (Narrow AI, General AI, Generative AI) - AI business value and use cases - AI concepts and terminology |
| AI Risk and Performance Management | 20% | - Technical debt in AI projects - Model performance metrics - Monitoring and maintenance planning - AI failure modes and mitigation - AI-specific risk identification |
| AI Team and Stakeholder Management | 20% | - Managing AI specialist expectations - Cross-functional collaboration - Communication in AI projects - Stakeholder engagement strategies - AI team roles and skills |
| AI Governance and Ethics | 20% | - Responsible AI practices - Transparency and explainability - AI governance structures - Regulatory compliance considerations - Bias identification and mitigation - AI ethics principles and frameworks |
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