NCA-GENL Experimentation Practice Question
During an ablation study, a team removes the instruction-tuning stage from their NeMo pipeline and observes that the model still answers factual questions but frequently ignores the requested output format. They want to attribute this change in behavior to the removed stage rather than to noise. Which experimental design element is most important for supporting that attribution?
⚠ Common exam trap
The trap here is adding extra training or a different prompt to the ablated variant, which introduces a second difference and destroys the ability to attribute the outcome to the removed stage.
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
✓
Run both the full pipeline and the ablated pipeline with all other variables held constant, then compare on the same evaluation set.
An ablation is only interpretable when the manipulated component is the sole difference between conditions. Keeping data, hyperparameters, training budget, and evaluation protocol identical, and scoring both variants on the same held-out set, lets the team attribute the observed format-compliance change to removing instruction tuning instead of to unrelated variation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the ablated model's training steps so it receives more optimization than the full pipeline.
Why it's wrong here
Giving the ablated variant extra training introduces a confounding variable: any difference in behavior could stem from the additional optimization rather than from removing instruction tuning. A valid ablation keeps training budget identical across conditions so the removed stage is the only meaningful difference.
- ✗
Retrain the ablated model several times with different random seeds and report the highest format-compliance score.
Why it's wrong here
Varying seeds addresses variance, but reporting the highest score cherry-picks a favorable run and inflates the ablated model's apparent ability. More importantly, without a matched full-pipeline baseline evaluated identically, there is still no controlled contrast that would let the team attribute the format behavior to the removed stage.
- ✓
Run both the full pipeline and the ablated pipeline with all other variables held constant, then compare on the same evaluation set.
Why this is correct
Holding every other factor constant and evaluating both variants on identical data isolates the instruction-tuning stage as the only difference between conditions. Any systematic change in format compliance can then be attributed to that stage rather than to confounding changes in data, hyperparameters, or evaluation setup.
- ✗
Evaluate the ablated model with a different, more format-focused prompt template than the full pipeline.
Why it's wrong here
Changing the prompt template between conditions means the comparison mixes two differences at once, the removed stage and the prompt change. If format compliance improves, the team cannot tell which factor caused it, so the attribution the study is trying to establish would be unsupported.
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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
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.