NCA-GENL Experimentation Practice Question
A data scientist is preparing an LLM fine-tuning experiment on NVIDIA NeMo and wants every run to be reproducible weeks later. The team's experiment tracker currently logs only the final validation loss. Which additional item is most important to record so a run can be reproduced exactly?
⚠ Common exam trap
The trap here is assuming that saving final metrics or dashboards is equivalent to experiment tracking, when reproducibility actually requires the seed and full configuration inputs.
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
✓
The random seed, framework version, and full training configuration
Exact reproduction of an LLM experiment depends on controlling every stochastic and environmental input. The seed governs initialization and data ordering, the framework and CUDA versions govern kernel behavior and numerics, and the full configuration governs optimization. Logging only a final metric captures an outcome but discards the recipe, so the run cannot be recreated reliably.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The wall-clock duration of the final epoch only
Why it's wrong here
Epoch duration is a performance metric that can vary with cluster load, thermal conditions, and competing jobs, so it cannot reconstruct a run. It says nothing about hyperparameters, data order, or library versions. Logging only timing metadata leaves the experiment irreproducible because none of the inputs that determine model weights are captured.
- ✓
The random seed, framework version, and full training configuration
Why this is correct
Reproducibility requires capturing the stochastic and environmental inputs: the random seed used for data shuffling and initialization, the exact NeMo and CUDA versions, and the complete training configuration such as learning rate, batch size, and precision. Without these, rerunning the job can produce different weights and metrics even with identical data, making comparisons across experiments unreliable.
- ✗
A screenshot of the loss curve rendered in the dashboard
Why it's wrong here
A loss curve image is a visualization of an outcome, not an input. It cannot restore the seed, hyperparameters, or software stack, and it may be downsampled or smoothed. While useful for human review, it provides no mechanism to rerun the experiment and obtain identical weights, so it does not satisfy the reproducibility requirement.
- ✗
The names of the engineers who launched each training job
Why it's wrong here
Recording who launched a job is useful for audit and ownership but has no effect on the numerical outcome of training. Two engineers running the same configuration with the same seed on the same hardware will get the same result. The question asks what enables exact reproduction, and personnel metadata does not control any computation in the training loop.
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
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.