NCA-GENL Experimentation Practice Question
An enterprise AI researcher is conducting ablation studies on a large language model using NVIDIA NeMo. To ensure the experimentation results are scientifically valid and statistically sound, which THREE practices must be enforced during the study?
⚠ Common exam trap
Candidates often overlook the random initialization seed. By failing to repeat experiments with different seeds, they risk attributing performance changes to their variable rather than to stochastic training noise.
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 across multiple random initialization seeds to account for stochastic variance in training.
Rigorous ablation studies require isolating individual components while holding all other experimental variables constant. Enforcing multiple random seeds accounts for initialization variance, controlling confounding hyperparameters prevents attribution errors, and applying rigorous statistical testing confirms whether observed performance deltas are statistically significant.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vary multiple architectural hyperparameters simultaneously in every single experimental iteration.
Why it's wrong here
Varying several hyperparameters at once confounds the results, because no single change can be attributed to the observed effect. Ablation isolates one variable per run while holding others fixed. Simultaneous sweeping suits coarse hyperparameter search, where interaction effects are the goal, not causal attribution.
- ✓
Evaluate across multiple random initialization seeds to account for stochastic variance in training.
Why this is correct
Deep learning models exhibit sensitivity to initial weight distributions and data shuffling order. Running evaluations across multiple distinct random seeds ensures that reported performance improvements reflect genuine architectural gains rather than fortunate random initialization artifacts.
- ✓
Keep all non-target hyperparameters strictly constant while isolating the variable under study.
Why this is correct
Isolating the experimental factor by keeping learning rates, context windows, and optimization algorithms constant ensures valid attribution. Any variation in output metrics can then be directly and reliably correlated with the specific component being ablated.
- ✓
Apply appropriate statistical significance testing to validate performance differences between variants.
Why this is correct
Small numerical fluctuations in benchmark scores can often be attributed to random noise rather than meaningful algorithmic improvements. Applying statistical tests such as t-tests or bootstrapping confirms whether an observed performance delta is genuinely significant.
- ✗
Discard all experimental runs that fail to meet performance expectations without logging the failure.
Why it's wrong here
Discarding failed runs without logging them introduces survivorship bias and destroys reproducibility, since the negative results are exactly what the ablation is measuring. Logging every run, including failures, is required. Discarding is tempting to keep result tables clean, but that suits reporting, not scientific validity.
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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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