NCA-GENL Study Guide: NVIDIA Generative AI & LLMs

The NCA-GENL is NVIDIA's associate exam for people building with generative AI and large language models. It's 60 minutes, 50 to 60 multiple-choice questions, and it costs $125. If you've already worked with LLMs or done some ML, two to four weeks of focused study gets most people there.

The full name is NVIDIA-Certified Associate: Generative AI and LLMs. It's aimed at AI and LLM developers, junior ML engineers, prompt engineers, and software developers moving into generative AI. The only stated prerequisite is a basic understanding of generative AI and large language models, so you don't need years of research behind you, but you do need to have actually built or experimented with these models.

Try a few realistic NCA-GENL questions and you'll see which domain needs the most work.

NCA-GENL exam logistics

NVIDIA doesn't publish a pass mark for this exam, so don't fixate on a target percentage. Aim to be solid across every domain. With up to 60 questions in 60 minutes you have about a minute each, which is fine if you keep moving and don't get stuck.

What's on the NCA-GENL exam

Five domains. Core ML knowledge and software development together make up more than half the exam, so weight your prep that way.

Core Machine Learning and AI Knowledge (30%) is the foundation: how models learn, how transformers and LLMs work under the hood, the difference between training and inference, and the vocabulary of generative AI. This is the single biggest slice, so make sure the fundamentals are genuinely solid rather than half-remembered.

Software Development (24%) is building with these models in practice. Work in Python, lean on the common libraries and APIs, wire a model into an actual application. Hands-on experience pays off here. If you've shipped something that calls an LLM, you're most of the way there.

Experimentation (22%) covers prompting, fine-tuning, and evaluating models, the iterative loop of getting a model to do what you want and measuring whether it did. Know how you'd compare two approaches and judge which is better.

Data Analysis and Visualization (14%) is preparing and exploring the data that feeds these systems, and making sense of results. Smaller weight, but it overlaps with everyday ML work.

Trustworthy AI (10%) is the responsible-AI material: bias, safety, and the limits of these models. It's the smallest domain, but the concepts are quick to learn and easy points if you don't skip them.

How hard is the NCA-GENL and how long to prepare

It's an associate exam, so it favors breadth over depth. The catch is that "generative AI" spans theory, coding, and experimentation, and most people are strong in one of those and thinner in the others. A developer who calls LLM APIs daily might be shaky on the underlying ML; someone from a data background might be rusty on shipping software. Figure out which you are and spend your time on the gap.

If you've built things with LLMs, two to four weeks is realistic. If generative AI is newer to you, give it longer and do more hands-on work, because the questions assume you've actually used these tools, not just read about them.

The roles this maps to pay well. Indeed put the average AI/ML engineer salary around $148,324 per year in the US (May 2026), and one breakdown by experience runs from $70,000 to $90,000 entry level up to $120,000 to $160,000 senior (AnalyticsVidhya, 2024). Generative-AI and LLM job postings are plentiful too, with thousands of openings listed across the major job boards in mid-2026. Treat those as ranges, not promises, but the demand is clearly there.

A 4-week NCA-GENL study plan

Week 1: core ML and LLM foundations. Make sure you understand how LLMs and transformers work, training versus inference, embeddings, and the standard generative-AI vocabulary. This domain is 30% of the exam, so it's worth the time. NVIDIA's Deep Learning Institute courses and the official NCA-GENL study guide are your primary sources.

Week 2: software development with LLMs. Build something small that calls a model: prompt it, handle the output, and integrate it into an app. Get comfortable with Python and the common libraries. The point is to make the "how do you actually use this" questions feel obvious.

Week 3: experimentation and data. Work through prompting techniques, the basics of fine-tuning, and how you'd evaluate and compare models. Then cover the data analysis and Trustworthy AI material: data prep, visualization, bias, and safety.

Week 4: practice and weak spots. Switch to timed, exam-style questions. For every one, right or wrong, read why each option is correct or not. Track what you keep missing and drill it. Practice with realistic NCA-GENL questions until the phrasing stops surprising you.

What trips people up

The most common mistake is leaning entirely on hands-on experience and skipping the theory. You can build with LLMs every day and still miss questions about how they actually work, and that's a 30% domain. Read the fundamentals even if the coding feels easy.

The opposite trap catches the theory-strong crowd: knowing the concepts but never having built anything. A quarter of the exam is software development, and it shows when someone has only read about calling a model rather than done it. Write a little code before exam day.

And don't chase a passing score. NVIDIA doesn't publish one, so being steady across all five domains beats betting you can carry a weak area. The exam rewards understanding the whole generative-AI workflow, from the model to the application to the data, more than deep expertise in any single corner.

Practice the right way

Reading the study guide gets you the concepts. Answering exam-style questions is what turns them into a pass, because it exposes the domains you only think you know. Drill timed sets, read every explanation, and keep a running list of what you miss.

When you're ready to test yourself, practice NCA-GENL with realistic, exam-style questions, or browse the full catalog to plan what comes after your first NVIDIA cert. When you're ready to level up, the professional-tier generative AI and LLMs certification is the natural next step.

Frequently asked questions