NCA-GENL Experimentation Practice Question
An ML engineer is setting up an experiment log for a fine-tuning run and wants to record the metadata necessary to reproduce the resulting model later. Which two items are most essential to capture for reproducibility? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse operational telemetry such as epoch duration or reviewer names with the configuration metadata that actually determines model weights.
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 used for data shuffling and initialization.
Reproducibility requires capturing the inputs that determine the trained weights: the randomness source and the exact training data. The seed governs shuffling and initialization, and the dataset version pins the examples used. Together they let another engineer rerun the same configuration and obtain a comparable model, which is the practical definition of a reproducible experiment.
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 names of the engineers who reviewed the training logs.
Why it's wrong here
Reviewer identity is an audit-trail detail, not a factor that shapes the trained weights. Recording it does not help another person recreate the model, because the outcome depends on data, configuration, and randomness rather than on who inspected the logs. This item is not part of the essential reproducibility metadata for a fine-tuning run.
- ✓
The random seed used for data shuffling and initialization.
Why this is correct
The random seed controls data ordering and parameter initialization, both of which materially affect the trained result. Without recording it, a later rerun may produce a different model even with identical code and data. Capturing the seed is therefore one of the minimum metadata items needed for a reproducible fine-tuning experiment.
- ✗
The wall-clock duration of each training epoch.
Why it's wrong here
Epoch duration depends on hardware, cluster load, and batch scheduling, and it does not influence the learned parameters. Two runs with identical seeds, data, and hyperparameters can have very different epoch times yet produce equivalent models. Capturing this metric is useful for performance analysis but is not essential for reproducing the model itself.
- ✗
The GPU model and driver version used during training.
Why it's wrong here
Hardware and driver details can affect numerical determinism at the margins, but they are not the primary determinants of the learned result. Identical configuration on different but compatible hardware usually yields a functionally equivalent model. For the essential reproducibility metadata set, the seed and dataset version carry far more weight than the specific GPU model.
- ✓
The exact dataset version or snapshot identifier used for training and validation.
Why this is correct
Datasets change over time through additions, corrections, and filtering, so a version or snapshot identifier pins the exact examples the model saw. Without it, reproducing the run is impossible because the training distribution may have shifted. Recording the dataset version is essential metadata for any reproducible LLM fine-tuning experiment.
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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.