NVIDIA NCA-GENL: NVIDIA-Certified Associate: Generative AI LLM

Last checked October 22, 2026 against the official NVIDIA exam page.

1. Question 1Prompt engineering and model adaptation

   A developer wants an LLM to return a JSON object with specific keys for every request. Which prompting approach best achieves consistent output?

   1. AAsk for 'any format' and parse whatever comes back with a regex fallback
   2. BSpecify the exact output schema in the prompt and provide an example of valid output
   3. CRemove all instructions from the prompt so the model can respond freely without format constraints
   4. DUse the highest temperature so the model is creative and varied across runs
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   Correct answer: B

   An explicit schema and an example guide consistent structured output. Allowing any format, high temperature and removing instructions reduce consistency.

2. Question 2Core machine learning and LLM concepts

   A language model is trained to predict the next token in a sequence. What is the main training objective used for most decoder-only LLMs?

   1. APredicting the image class of each token
   2. BMaximizing the length of the context window
   3. CNext-token prediction with cross-entropy loss
   4. DMinimizing the number of tokens in the output
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   Correct answer: C

   Decoder-only LLMs are trained to predict the next token, usually with cross-entropy loss over the vocabulary. The other objectives do not describe standard LLM pretraining.

3. Question 3Data preparation and evaluation

   A dataset for training an LLM contains many near-duplicate documents. What problem can this create?

   1. AFaster convergence in early epochs, with no effect on evaluation scores or loss curves.
   2. BOverrepresentation of some content and inflated evaluation results if duplicates leak across splits
   3. CAutomatic removal of bias, since repeated text is averaged out during gradient steps.
   4. DSmaller vocabulary size, because the tokenizer merges frequent duplicate strings into shorter subword units.
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   Correct answer: B

   Duplicates overweight some content and can leak into evaluation sets, inflating scores. Faster convergence and bias removal are not outcomes, and vocabulary size is set by the tokenizer.

4. Question 4Deployment and trustworthy AI

   A company wants to track how an LLM application performs after launch, including quality, latency, and cost. Which practice is most appropriate?

   1. AMonitoring dashboards with defined metrics, sampling of outputs for review, and alerts
   2. BTurning off most metrics to reduce logging overhead on production servers
   3. CRunning a manual review of the application once a year during the annual planning cycle and budgeting
   4. DRelying on user complaints and support tickets to surface problems quickly
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   Correct answer: A

   Defined metrics, output sampling and alerts provide ongoing visibility. Annual checks, complaint-only monitoring and disabled metrics hide problems.

5. Question 5Prompt engineering and model adaptation

   A team wants to adapt a large LLM to a specialized domain without retraining all weights. Which technique adds small trainable low-rank matrices to the frozen model?

   1. AFull retraining from random initialization
   2. BLoRA
   3. CTokenizer replacement
   4. DGreedy decoding
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   Correct answer: B

   LoRA trains small low-rank adapters while keeping most weights frozen, which lowers cost. Random initialization retrains everything, decoding is inference, and tokenizer replacement changes the input representation.