Free reasoning-led diagnostic
NVIDIA-Certified Professional: Agentic AI (NCP-AAI) Mock Exam & Readiness Assessment
A free 30-question diagnostic for NVIDIA-Certified Professional: Agentic AI (NCP-AAI). Answer at your own pace, then review a domain-by-domain readiness signal with fully explained reasoning for every question.
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- 30 Questions
- 25 Single-Answer / 5 Multi-Answer
- 10 Domains
- 55 minutes (~1.8 minutes per question based on the official exam)
Who This Diagnostic Is For
This assessment is for learners preparing for NVIDIA-Certified Professional: Agentic AI (NCP-AAI) who want an honest, evidence-based signal of where they stand today. Every question is original CertShield content, technically validated against current official documentation (Live version effective August 7, 2026) and peer-reviewed by subject-matter experts before publication.
What It Measures
| Domain | Questions |
|---|---|
| Agent Architecture and Design | 5 |
| Agent Development | 5 |
| Cognition, Planning, and Memory | 3 |
| Deployment and Scaling | 4 |
| Evaluation and Tuning | 4 |
| Human-AI Interaction and Oversight | 1 |
| Knowledge Integration and Data Handling | 3 |
| NVIDIA Platform Implementation | 2 |
| Run, Monitor, and Maintain | 2 |
| Safety, Ethics, and Compliance | 1 |
Limitations of this diagnostic: 30 questions cannot cover every exam objective with equal depth. Domains with fewer than three questions are labelled "Limited evidence" in your results and can never cause a readiness downgrade on their own. This is not an official exam, an accredited course, or a pass-rate prediction.
Technically validated: Completed August 26, 2026, against the live NVIDIA NCP-AAI certification page (nvidia. See the assessment methodology for how scoring, confidence and readiness safeguards work.
About the Official NVIDIA-Certified Professional: Agentic AI Exam
Sourced directly from the official NVIDIA-Certified Professional: Agentic AI Exam Guide — Live version effective August 7, 2026.
- Questions
- 60‑70 scored questions
- Time Limit
- 120 minutes
- Passing Score
- Not publicly disclosed by NVIDIA
- Registration Fee
- $200
- Delivery
- Online, proctored remotely
- Validity
- 2 years from issuance
Official exam domains this diagnostic mirrors:
- Agent Architecture and Design 15%
- Agent Development 15%
- Evaluation and Tuning 13%
- Deployment and Scaling 5%
- Cognition, Planning, and Memory 10%
- Knowledge Integration and Data Handling 10%
- NVIDIA Platform Implementation 7%
- Run, Monitor, and Maintain 7%
- Safety, Ethics, and Compliance 5%
- Human-AI Interaction and Oversight 5%
Who this certification is for:
NVIDIA's own exam study guide describes the ideal candidate as an intermediate AI/ML practitioner who designs, evaluates, and deploys autonomous agentic AI systems in production, typically with 2-3 years of experience in AI/ML roles and hands-on work on production-level agentic AI projects.
Frequently Asked Questions
How many questions are on the NVIDIA-Certified Professional: Agentic AI (NCP-AAI) exam?
60‑70 scored questions
How long is the NVIDIA-Certified Professional: Agentic AI (NCP-AAI) exam?
120 minutes
What is the passing score for NVIDIA-Certified Professional: Agentic AI (NCP-AAI)?
Not publicly disclosed by NVIDIA
How much does the NVIDIA-Certified Professional: Agentic AI (NCP-AAI) exam cost?
$200
What topics are covered in the NVIDIA-Certified Professional: Agentic AI (NCP-AAI) exam?
The exam covers Agent Architecture and Design 15%, Agent Development 15%, Evaluation and Tuning 13%, Deployment and Scaling 5%, Cognition, Planning, and Memory 10%, Knowledge Integration and Data Handling 10%, NVIDIA Platform Implementation 7%, Run, Monitor, and Maintain 7%, Safety, Ethics, and Compliance 5%, and Human-AI Interaction and Oversight 5%.
Who is the NVIDIA-Certified Professional: Agentic AI (NCP-AAI) exam for?
NVIDIA's own exam study guide describes the ideal candidate as an intermediate AI/ML practitioner who designs, evaluates, and deploys autonomous agentic AI systems in production, typically with 2-3 years of experience in AI/ML roles and hands-on work on production-level agentic AI projects.
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