NCA-GENL — NVIDIA-Certified Associate

Prep for NCA-GENL

300 practice questions on Generative AI and Large Language Models, each with an explanation for all four options. Ten domains, six timed sets, and not a single network request.

Coming soon: App Store Coming soon: Google Play

• Exam NCA-GENL, Generative AI and LLMs (NVIDIA-Certified Associate)
• Questions 300, split into 6 practice sets of 50
• Explanations 1,200 — one per answer option
• Price USD 4.99, one-time. No in-app purchases
• Network None. The app never connects
• Platforms iOS and Android

The exam this app is for

NCA-GENL is the associate-level exam for people who build things on top of language models: the prompt, the retrieval layer, the evaluation harness and the served endpoint. It expects you to know enough of the machinery underneath — attention, positional information, why a transformer handles long-range dependencies differently from an RNN — to make sensible choices above it.

It is also the broadest of the three associate exams in this family: ten domains, from neural-network fundamentals to ethical AI, with NVIDIA's own software stack in the middle. Questions tend to describe a failure and ask for the cause or the fix. A chatbot cites a case that exists nowhere in the training data, the prompt or the vector store. Validation loss bottoms out at epoch four and climbs. A RAG system retrieves the right passage and still answers wrongly. Knowing the term is not enough; you have to know which term explains the symptom.

What the 300 questions cover

Ten domains, with the app's own distribution shown below. The shares are an editorial choice for practice, not an official blueprint.

• Experiment Design and Evaluation42 questions · 14%

  Precision and recall from a confusion matrix, what ROUGE misses when a summary paraphrases well, perplexity versus a task benchmark, and the caveats of letting an LLM grade answers on a 1–10 scale.

• Real-world Applications42 questions · 14%

  Designs that survive contact with production: routing tickets when the categories keep changing, summarising a document longer than the context window, keeping early turns of a conversation alive, and which review tasks suit an assistant at all.

• Machine Learning and Neural Networks36 questions · 12%

  Overfitting and early stopping, vanishing gradients and what actually fixes them, why a smaller model can beat a much larger one on a narrow task, and what hallucination is at the level of the weights.

• LLM Architecture30 questions · 10%

  Self-attention and why it is order-blind without positional information, multi-head attention, the position-wise feed-forward sublayer, and transformers versus recurrent models on long-range dependencies.

• Ethical AI30 questions · 10%

  Memorised training data surfacing verbatim, evaluation that breaks results down by user group, the reach of a corpus scraped from one country, and layered controls where a wrong answer can hurt someone.

• Prompt Engineering24 questions · 8%

  Few-shot examples that skew a model's decisions, instructions buried under pasted reference text, one prompt asked to do three jobs, and versioning prompts so quality stops drifting with every complaint.

• Experimentation and Training24 questions · 8%

  Transfer learning on a small labelled set, hyperparameter search within a fixed run budget, distillation onto a teacher's output distribution, and target leakage from a feature that only exists after the outcome.

• Retrieval-Augmented Generation24 questions · 8%

  Why you retrieve passages instead of pasting the corpus, overlapping chunks, stale answers after a document is replaced, and the case where retrieval is right and the generation is still wrong.

• Model Deployment24 questions · 8%

  Batch versus interactive serving, diagnosing CUDA out-of-memory under load, cold starts after scaling from zero, and matching the serving shape to the traffic it gets.

• NVIDIA Software24 questions · 8%

  Which component owns which job: Triton Inference Server against NIM, what TensorRT-LLM optimises, GPU-accelerated data curation for a pretraining corpus, and what NeMo Guardrails adds at runtime.

How you study with it

• Six sets of 50, each one drawing on all ten domains. With this many domains, a full set is the only honest test of whether the small ones stuck.
• Exam mode hides feedback until the end and runs a timer. Practice mode shows the explanation the moment you answer.
• Per-domain scoring matters more here than on a three-domain exam. The result screen will usually show one or two domains dragging the total down — often evaluation or the NVIDIA software names — and that is your next study session.
• Missed questions collect themselves into a review run, and the star bookmarks anything you want to come back to.
• Every option is explained, which is the point in an exam full of plausible distractors. Knowing why "increase the temperature" is wrong is the actual skill.

Questions people ask

Do I need to write code to pass this exam?

The questions in this app are about judgement rather than syntax: which technique fits, what a metric is telling you, why a pipeline behaves the way it does. There is no code to write inside the app. Having built something with an LLM will still make the scenarios feel familiar much faster.

How much maths is in it?

Only what you can do in your head. Precision and recall from a small confusion matrix, reading a loss curve, comparing two evaluation numbers. Nothing here needs a calculator, and the explanations walk through the arithmetic when there is any.

Does it name NVIDIA's software stack?

Yes — a whole domain of it, because the exam expects you to tell the components apart. The explanations describe what each one is for, so the questions are still useful if you have never deployed with that stack yourself.

How is this different from the NCA-GENM app?

Prep for NCA-GENM is about images, audio and video: diffusion models, encoders, multimodal data. This app stays with text and language models. The two banks are written separately and share no questions. There is also Prep for NCA-AIIO for infrastructure, and Prep for NCP-AAI at professional level.

Does it work offline?

Completely. All 300 questions and 1,200 explanations ship in the download, the app makes no network requests at all, and your progress never leaves the device. See the privacy policy.

Where do the questions come from?

They are written with AI and organised against the published NCA-GENL domains. They are original practice material, not real exam questions, and no exam question is reproduced. If something looks wrong, write to us and the correction ships in the next update.

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Disclaimer. Prep for NCA-GENL is an independent study aid. It is not affiliated with, authorized by, endorsed by, or sponsored by NVIDIA Corporation. NVIDIA, NCA-GENL and the NVIDIA-Certified Associate program name are trademarks or certification program names of NVIDIA Corporation, used here only to identify the exam this app helps you prepare for. No certification result is guaranteed.