NVIDIA NCA-GENL Exam Practice Questions, practice question pack
NVIDIA · NCA-GENL

NVIDIA NCA-GENL Exam Practice Questions

50 questionsPDF by emailUpdated September 2026

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Three of the 50 questions in this pack

Question 1

Which of the following best describes Word2vec?

  1. A programming language used to build artificial intelligence models.
  2. A statistical technique used to analyze word frequency in a text corpus.
  3. A deep learning algorithm used to generate word embeddings from text data.
  4. A database management system designed for storing and querying word data.
Show answer and explanation

Correct answer: C. A deep learning algorithm used to generate word embeddings from text data.

embeddings from text data. Word2vec is a deep learning algorithm that generates word embeddings by learning dense vector representations of words from large text corpora. It uses neural networks to capture semantic relationships between words, allowing similar words to have similar vector representations. This enables downstream NLP tasks to leverage these pre-trained embeddings.

Why the other options are wrong

  • A. Word2vec is not a programming language; it is a specific algorithm implemented in libraries like TensorFlow or Gensim.
  • B. While word2vec analyzes text, it is not merely a statistical frequency analysis technique; it learns semantic meaning through deep learning.
  • D. Word2vec is not a database management system but rather an algorithm for generating vector representations of words.

Question 2

In the context of language models, what does an autoregressive model predict?

  1. The probability of the next token in a text given the previous tokens.
  2. The probability of the next token using a Monte Carlo sampling of past tokens.
  3. The next token solely using recurrent network or LTSM cells.
  4. The probability of the next token by looking at the previous and future input tokens.
Show answer and explanation

Correct answer: A. The probability of the next token in a text given the previous tokens.

previous tokens. Autoregressive models in language modeling predict the probability of the next token conditioned on all previous tokens in the sequence. This sequential dependency is fundamental to how models like GPT generate text one token at a time, with each prediction depending on the history of previously generated tokens.

Why the other options are wrong

  • B. While Monte Carlo sampling can be used in inference, it is not the defining characteristic of autoregressive prediction; the core mechanism is conditional probability based on previous tokens.
  • C. Autoregressive models are not limited to recurrent networks or LSTMs; transformer- based models like GPT are also autoregressive despite using attention mechanisms.
  • D. Autoregressive models specifically use only previous tokens, not future tokens; models that use both directions are called bidirectional or non-autoregressive.

Question 3

In large-language models, what is the purpose of the attention mechanism?

  1. To measure the importance of the words in the output sequence.
  2. To determine the order in which words are generated.
  3. To capture the order of the words in the input sequence.
  4. To assign weights to each word in the input sequence.
Show answer and explanation

Correct answer: D. To assign weights to each word in the input sequence.

sequence. The attention mechanism in language models assigns weights to each word in the input sequence to determine how much each input token should contribute to the computation of the output. These weights allow the model to focus on relevant parts of the input when producing each output token, capturing long-range dependencies more effectively than recurrent architectures.

Why the other options are wrong

  • A. While attention does relate to importance, it operates on input words to influence output computation, not directly measuring importance of output words.
  • B. The attention mechanism does not determine the order of word generation; that is controlled by the autoregressive decoding process.
  • C. Positional encoding, not attention, is primarily responsible for capturing the order of words in the input sequence.

See all 10 free questions Get the full pack, US$39

50 practice questions for NVIDIA Generative AI LLMs Associate (NCA-GENL), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 50 questions mapped to the NCA-GENL exam objectives, across the full topic list
  • Answers and explanations for every question, including why each wrong option is wrong
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

An NCA-GENL attempt costs US$125. This pack is US$39, paid once.

Try 10 questions free before you buy.

Last updated September 2026 · 50 questions

What makes the NCA-GENL hard

The NCA-GENL is NVIDIA’s large language model fundamentals exam, and it is more technical than the associate label suggests: transformer internals show up by name, not by vibe.

Expect precision questions on attention mechanisms and why transformers replaced RNNs, tokenisation and embeddings, fine-tuning versus prompt engineering versus RAG trade-offs, and experiment design, including train and test splits, metrics and overfitting signals. Then there is the NVIDIA layer that generic LLM courses skip: where NeMo, TensorRT-LLM and Triton fit into the stack, plus a section on trustworthy AI.

It runs to around 50 questions in an hour, which leaves little time to work out unfamiliar terminology on the day.

About the exam

NCA-GENL is NVIDIA’s associate-level certification for generative AI and large language models: core machine learning and transformer concepts, prompting, fine-tuning, data handling, experimentation and trustworthy AI, with the NVIDIA software stack in view. There are no formal prerequisites. Valid for two years.

Exam topics

  • Machine learning and neural network fundamentals
  • Transformer architecture, tokenisation, and embeddings
  • Prompt engineering, fine-tuning, and RAG approaches
  • Data analysis, experimentation, and evaluation
  • NVIDIA software stack and trustworthy AI

Around 50 questions, 60 minutes, pass or fail result, no numeric score published, online remote proctored via Certiverse, US$125 per attempt, valid for two years.

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Questions before you buy

What do I get when I buy the NVIDIA NCA-GENL pack?

50 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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