NCA-GENL Experimentation Practice Question
An engineer is setting up an automated experiment sweep over temperature and top-p for a NeMo-served LLM, and wants the results to be comparable and reproducible. Which two practices should be applied? (Choose two.)
⚠ Common exam trap
The trap here is believing that more randomness or more model capacity makes a sweep more informative, when uncontrolled variation actually prevents attributing results to the sampling parameters under test.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the same prompt set and maximum token count across all temperature and top-p combinations.
Comparable decoding sweeps require controlling both stochasticity and inputs. Fixing the sampler seed makes each configuration repeatable, while holding prompts and maximum token count constant ensures the only varying factors are temperature and top-p. Changing the model, the prompt template, or removing parameter logs introduces confounds or destroys traceability, so those practices undermine the experiment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the same prompt set and maximum token count across all temperature and top-p combinations.
Why this is correct
Holding the input prompts and generation length constant isolates the sampling parameters as the only variables. If prompt sets or output lengths vary between trials, observed quality differences may stem from the inputs rather than from temperature or top-p. Controlled inputs are required for a valid comparison of decoding settings.
- ✗
Allow each trial to choose its own prompt template so the model can show its best behavior.
Why it's wrong here
Letting trials use different prompt templates introduces a second variable that can easily dominate the effect of temperature and top-p. A favorable template might make one configuration look better even if the decoding setting is not the cause. Controlled experiments require identical prompts so the sampling parameters are the only differences.
- ✓
Fix the random seed used by the generation sampler for every trial in the sweep.
Why this is correct
Fixing the sampler seed makes stochastic decoding repeatable, so differences between trials can be attributed to temperature and top-p rather than random draw variation. Without a fixed seed, a trial that happens to sample a good path could appear superior for the wrong reason. Seed control is a foundational reproducibility practice when comparing generation configurations.
- ✗
Increase the model's parameter count for trials with higher temperature to compensate for randomness.
Why it's wrong here
Changing the model between trials destroys comparability because model capability becomes a confound alongside temperature. Temperature affects the shape of the sampling distribution, not the model's knowledge, so compensating with more parameters is conceptually wrong. A sweep over decoding parameters must keep the underlying model fixed across all trials.
- ✗
Disable logging of per-trial parameters to reduce storage overhead during the sweep.
Why it's wrong here
Omitting parameter logs makes it impossible to reconstruct which configuration produced which output, undermining reproducibility and auditability. Storage savings are trivial compared with the cost of an uninterpretable sweep. Recording temperature, top-p, seed, and prompt identifiers for every trial is essential for later analysis and replication.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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