NCA-GENL Experimentation Practice Question
Which THREE factors influence the reproducibility of an LLM experiment?
⚠ Common exam trap
Candidates frequently forget that hardware configurations or infrastructure metrics alone do not dictate LLM reproducibility without locking down random seeds and framework versions.
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
✓
Random seed initialization
Reproducibility requires strict control over the experimental environment. Because LLM training involves non-deterministic factors, controlling the random seeds, the exact data splits, and the library versions is paramount. Professional experiment tracking requires the ability to recreate identical results, which is only possible when every component of the pipeline is version-controlled and explicitly defined in the experiment's configuration manifest.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Random seed initialization
Why this is correct
In deep learning, random seeds affect initialization, data shuffling, and dropout masks. Setting a fixed seed ensures that the stochastic elements of the training run are deterministic, which is essential for verifying that performance improvements are due to algorithmic changes rather than the random state of the model parameters.
- ✗
The ambient temperature of the datacenter
Why it's wrong here
While hardware performance might fluctuate slightly with temperature, it is not a factor that researchers can or should control for experiment reproducibility. Focusing on irrelevant physical parameters distracts from the necessary software-level controls, such as code versions, dependency management, and random seed setting, which are the real drivers of reproducibility.
- ✓
Version of the deep learning framework
Why this is correct
Different versions of frameworks like PyTorch or NeMo can change the implementation of operators, impacting precision and performance. For an experiment to be reproducible, the exact library versions must be captured. Even minor updates can introduce subtle numerical changes that lead to different model behaviors over thousands of training steps.
- ✓
Data preprocessing and splitting strategy
Why this is correct
Data is the most important factor in training. If the training/validation split changes or the preprocessing pipeline is modified, the model will see different information, making it impossible to compare performance across runs. Consistent data handling is a prerequisite for any reproducible experimentation in the generative AI domain.
- ✗
The number of hours the researcher works
Why it's wrong here
The researcher's working hours are irrelevant to the technical reproducibility of the experiment. Reproducibility is a function of the code, data, and environment settings. Managing the team's time is a management task that has no relationship to the deterministic nature of the training and validation processes in AI.
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.