NVIDIA-Certified Associate: Generative AI LLMs exam guide

Who it's for

Developers, data scientists and technical professionals with a basic grasp of generative AI who want an entry-level, vendor-backed LLM credential.

What the exam covers

Eligibility & prerequisites

Required: None.

Recommended: Basic understanding of generative AI and LLMs; NVIDIA recommends Fundamentals of Deep Learning and Rapid Application Development With LLMs.

Keeping it active

Valid 2 years from issuance. Recertify by retaking the current version of the exam before it expires.

Skills & careers

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

1. Week 1: Refresh ML basics: neural networks, training loops, loss, overfitting, embeddings and tokenization.
2. Week 2: Transformers and LLMs: attention, decoding strategies, fine-tuning vs prompting, PEFT/LoRA at concept level.
3. Week 3: Take NVIDIA's Rapid Application Development With LLMs course; build a small RAG app with an NVIDIA NIM or open model.
4. Week 4: Experimentation and evaluation metrics (perplexity, BLEU/ROUGE, human eval), data analysis and trustworthy AI topics; take practice questions and review weak areas.

Exam-day tips

• Core ML and AI knowledge is 30% of the exam, so do not skip classical ML and deep learning basics in favour of only LLM topics.
• Expect questions that name NVIDIA tools (NeMo, NIM, TensorRT-LLM, Triton); know what each is used for at a high level.
• With about one minute per question, flag long scenarios and return later rather than stalling.
• Know trustworthy AI concepts such as bias, guardrails, privacy and explainability; they make up 10% of the score.

Key terms for this exam

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Commonly confused on this exam

Frequently asked questions