NCA-GENL Experimentation Practice Question
A research team is comparing three fine-tuning recipes for a NeMo LLM and wants the comparison to be defensible in a later review. Which two practices most improve the credibility of the reported comparison? (Choose two.)
⚠ Common exam trap
The trap here is believing that a higher reported score proves a better recipe, when unequal tuning effort or a contaminated evaluation set can produce that score without any real advantage.
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
✓
Evaluate every recipe on the same held-out evaluation set with identical decoding settings.
Credible comparisons rest on two pillars: full provenance of each run so results are reproducible, and a shared, uncontaminated evaluation protocol so scores are measured identically. Provenance lets reviewers attribute differences to the recipes, while a fixed held-out set with consistent decoding settings ensures the numbers being compared actually measure the same thing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Tune each recipe's learning rate separately until its score is maximized.
Why it's wrong here
Giving each recipe its own extensive tuning budget confounds the comparison: differences may reflect unequal tuning effort rather than the recipe design. A defensible comparison either fixes the tuning protocol across recipes or reports the search budget used for each so the reviewer can judge fairness.
- ✗
Reuse the evaluation set during training as an additional validation signal.
Why it's wrong here
Using the evaluation set during training contaminates it, so its scores no longer estimate performance on unseen data. The comparison would then reward recipes that adapted most to those specific examples, which is exactly the leakage that undermines the credibility the team is trying to establish.
- ✗
Report only the single best run for each recipe to keep the summary concise.
Why it's wrong here
Reporting only the best run hides run-to-run variance and overstates expected performance. A reviewer cannot tell whether a recipe is reliably better or simply benefited from a favorable random draw. Summarizing across seeds with mean and spread gives a far more honest and defensible picture.
- ✓
Evaluate every recipe on the same held-out evaluation set with identical decoding settings.
Why this is correct
Using one fixed evaluation set with identical decoding parameters ensures the recipes are judged on the same inputs under the same conditions, which isolates the effect of the recipe itself. If evaluation data or decoding settings differ between recipes, score differences may reflect the measurement setup rather than the training method.
- ✓
Log the exact dataset version, tokenizer, base checkpoint, and hyperparameters for every run.
Why this is correct
Recording dataset version, tokenizer, base checkpoint, and hyperparameters makes each run reconstructable, so a reviewer can verify that differences in results come from the recipes rather than from unnoticed configuration drift. Without this record, a comparison is anecdotal because no one can reproduce the conditions that produced each number.
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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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