NVIDIA NCP-AAI Exam Guide: What's on the Agentic AI Certification

What the NCP-AAI certification is

NCP-AAI — NVIDIA-Certified Professional: Agentic AI — is a professional-level, proctored certification that validates your ability to architect, develop, deploy, and govern production agentic AI systems. "Agentic" means systems where an LLM plans, calls tools, keeps memory, and acts toward a goal over multiple steps — not single-shot prompting. The exam spans the full lifecycle: multi-agent orchestration, reasoning and memory, tool use, retrieval (RAG), evaluation, deployment, and safety.

Exam format at a glance

The exam is roughly 70 questions delivered over 120 minutes, costs $200 USD, and is taken under proctored conditions. It covers 10 weighted domains — your score is effectively the weighted sum of how you do across them, so the heavier domains matter most.

Exam:        NVIDIA-Certified Professional: Agentic AI
Code:        NCP-AAI
Questions:   ~70
Duration:    120 minutes
Price:       $200 USD
Format:      Proctored, multiple-choice
Domains:     10 (weighted — see table below)

The 10 exam domains and their weights

Every NCP-AAI question maps to one of these ten domains. The four heaviest — Agent Architecture & Design, Agent Development, Evaluation & Tuning, and Deployment & Scaling — make up 56% of the exam between them, so that is where preparation pays off most.

Agent Architecture and Design ............ 15%
Agent Development ........................ 15%
Evaluation and Tuning .................... 13%
Deployment and Scaling ................... 13%
Cognition, Planning, and Memory .......... 10%
Knowledge Integration & Data Handling .... 10%
NVIDIA Platform Implementation ........... 7%
Run, Monitor, and Maintain ............... 5%
Safety, Ethics, and Compliance ........... 5%
Human-AI Interaction and Oversight ....... 5%

Who the exam is for

NCP-AAI targets AI engineers, ML engineers, solutions architects, and platform engineers who design and run agentic systems in production. You should be comfortable with LLM APIs, prompting, tool/function calling, vector retrieval, and the operational concerns of serving models — latency, cost, concurrency, and monitoring. It is not an entry-level exam; if you have never built an agent loop, start there first.

How to prepare efficiently

Because the exam is weighted, study in weight order. Lock down the four heavy domains first, then the two 10% domains (memory/planning and knowledge/data), then the lighter 5–7% domains last. The fastest way to find your gaps is a domain-weighted readiness test that mirrors the blueprint — it tells you which domains you are actually weak in rather than giving you a flat percentage.

Exam tip

Study in weight order, not alphabetical order. The four heaviest domains (Architecture & Design, Development, Evaluation & Tuning, Deployment & Scaling) are 56% of the exam — a strong showing there is most of a passing score. Do not over-invest in the 5% domains until the heavy ones are solid.