NVIDIA-Certified Professional: Generative AI LLMs

Leans towards model-level engineering: optimisation, fine-tuning and GPU acceleration, more than application building.

What it signals

Practical assessment: Scenario-based multiple choice. Remotely proctored multiple-choice.
Identity assurance: Proctored with identity verification. Online, remotely proctored (Certiverse)
Depth: Professional. Professional level.
Experience expected: 2 to 3 years. 2-3 years recommended.
Portability: Mostly transferable. Mostly general LLM engineering; GPU section leans on NVIDIA stack.
Verifiability: Public badge or registry. Credly badge and public directory.

For hiring managers

Leans towards model-level engineering (optimisation, fine-tuning, GPU acceleration = 44%) rather than application building; signals LLM infrastructure/performance skills more than app development.

Verifying it

Credly digital badge (accepted by the candidate) plus NVIDIA's public certification directory on Credly: https://www.credly.com/organizations/nvidia/directory

What it covers

Published exam outline

LLM Architecture 6%
Prompt Engineering 13%
Data Preparation 9%
Model Optimization 17%
Fine-Tuning 13%
Evaluation 7%
GPU Acceleration and Optimization 14%
Model Deployment 9%
Production Monitoring and Reliability 7%
Safety, Ethics, and Compliance 5%

By topic

GenAI apps: Substantial (20 to 35%). Prompt Engineering 13% + Evaluation 7% + LLM Architecture 6% = 26%.
Agents: Not covered. Agents not in the blueprint (covered by NCP-AAI).
Classical ML: Substantial (20 to 35%). Fine-Tuning 13% and Model Optimization 17% are model training/tuning work.
Operations: Substantial (20 to 35%). Deployment 9% + Monitoring 7% + GPU acceleration 14%.
Data: Touched on (under 10%). Data Preparation is 9%.
Responsible AI: Touched on (under 10%). Safety, Ethics, and Compliance is 5%.

Exam details

Who the provider says it is for. Software developers, software engineers, solutions architects, ML engineers, data scientists, AI strategists, generative AI specialists

Format: Multiple choice (count given as a range by NVIDIA)
Delivery: Online, remotely proctored (Certiverse)
Prerequisites: None formal
Recommended experience: 2-3 years in AI/ML roles working with LLMs; Python (plus C++ for optimisation), containers/orchestration; NVIDIA platform familiarity beneficial but not required
Renewal: Retake the exam

Preparing

Not confirmed

These details were not available from an official source at the time of research.

passingScore (not published)
Whether exam items include code snippets (not stated officially)
Original launch date

Sources (2)