NCA-GENL Experimentation Practice Question
A research team is running a hyperparameter sweep over learning rate and warmup steps for a NeMo fine-tuning job. They notice that runs with identical hyperparameters produce different final validation losses across repeated executions. Which two changes would most directly improve the reproducibility of these experiments? (Choose two.)
⚠ Common exam trap
The trap here is assuming that more logging or more hardware improves reproducibility, when the actual cause is uncontrolled stochasticity in the training pipeline.
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
✓
Enable deterministic kernels and disable nondeterministic cuDNN algorithms in the framework settings.
Run-to-run variance in identical configurations usually comes from uncontrolled randomness in data order, initialization, dropout, and nondeterministic GPU kernels. Fixing the random seed and enabling deterministic kernels address both the algorithmic and hardware-level sources. Adding GPUs, logging, or lowering the learning rate change the experiment or its observability but do not make repeated runs converge.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log the loss curve to TensorBoard so the team can visually compare runs.
Why it's wrong here
Visualization helps humans spot differences but does not change the underlying stochasticity of training. The runs would still diverge numerically; the team would simply see the divergence more clearly. Logging is an observability improvement, not a reproducibility fix.
- ✓
Enable deterministic kernels and disable nondeterministic cuDNN algorithms in the framework settings.
Why this is correct
Certain GPU kernels, especially in cuDNN, are nondeterministic by design for performance reasons, causing small numerical differences that accumulate over training steps. Enabling deterministic algorithms removes this source of variance, making repeated runs with the same seed produce identical or near-identical results. This is a standard reproducibility control.
- ✓
Set a fixed random seed for data shuffling, weight initialization, and dropout in the training configuration.
Why this is correct
Non-deterministic data order, initialization, and dropout are common sources of run-to-run variance. Pinning a seed in the NeMo config makes these stochastic elements repeatable, so identical hyperparameters yield comparable results. This directly addresses the observed variance without changing the experiment's scientific validity.
- ✗
Increase the number of GPUs per run so each experiment finishes faster.
Why it's wrong here
Adding GPUs changes the effective global batch size and the reduction order in distributed training, which can introduce additional numerical variance rather than reduce it. Faster completion does not make results more reproducible. Without adjusting batch size and seeds, this change could actually worsen the inconsistency the team is observing.
- ✗
Reduce the learning rate by a factor of ten across all sweep configurations.
Why it's wrong here
Lowering the learning rate changes the optimization trajectory but does not eliminate the stochastic sources causing identical configs to diverge. It may even mask the problem while making the sweep less informative about the intended hyperparameter range. This is a hyperparameter change, not a reproducibility control.
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JA
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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.