I took the NCA-GENL exam a few weeks ago. It’s definitely not just theory, but it’s also not hands-on like doing labs. Think of it more like scenario-based questions, they give you a setup (like a NeMo config or deployment pipeline) and ask what’s wrong, what you’d change, or what’s the expected output.
Here’s what I remember:
NeMo: You def need to know the structure of a NeMo model config, how training/inference works, and what each component does. They won’t ask you to write one, but they will give you one and ask you to spot issues or interpret it.
Triton/CUDA: Yeah, they show up. Triton questions were mostly about why/when to use it (batching, multiple models, performance stuff). CUDA was mostly high-level, don’t stress about code, just know what GPU acceleration enables and when it matters.
Some questions on LLMOps, like monitoring or scaling models in production (nothing too deep, but not really in the core training). A couple of evaluation metric questions, like perplexity vs BLEU vs accuracy. One or two touched on multi-modal models, which I wasn’t expecting.
What helped me most outside NVIDIA’s course was spending time on the NeMo GitHub repo, seriously, just browsing through the configs and README was really useful. I also watched a few NVIDIA GTC talks focused on NeMo and Triton, which helped me connect the theory to real-world deployment scenarios. Additionally, watching random YouTube breakdowns of LLM pipelines, even those from Hugging Face or OpenAI, gave me a better understanding of the big picture.
I also found some practice questions on CertBoosters while searching around, not a 1:1 match with the real exam, but it was helpful for testing how well I was retaining the concepts.
Honestly, if you understand the official course and how to apply it, you’ll be fine. Just don’t memorize, focus on understanding why you’d use a tool or set something up a certain way. Hope that helps, feel free to ask if you’re stuck on anything specific!