NCA-GENL Experimentation Practice Question
A research team is running a series of controlled LLM fine-tuning experiments on NVIDIA DGX systems using NeMo Framework to compare two learning-rate schedules. They want the comparison to be scientifically valid and repeatable by another engineer next quarter. Which two practices are required to make the experiments reproducible? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse performance optimizations, such as adding GPUs or enabling mixed precision, with the controls that actually make a training experiment reproducible.
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
✓
Record the exact dataset version, tokenizer configuration, and NeMo container image tag used.
Reproducibility in a controlled fine-tuning comparison requires controlling randomness and pinning the environment. Seeding all stochastic operations keeps data order, dropout, and initialization identical, while recording dataset version, tokenizer configuration, and container image tag lets another engineer rebuild the same setup. Performance-oriented choices such as GPU count, mixed precision, and inference benchmarking do not establish repeatability of the training comparison.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the resulting checkpoints to NVIDIA Triton Inference Server for latency benchmarking.
Why it's wrong here
Triton deployment measures inference latency and throughput after training, which is unrelated to reproducing the fine-tuning comparison. Serving benchmarks do not capture training seeds, dataset versions, or container tags, so they cannot help another engineer recreate the experiment. This activity belongs to a deployment evaluation phase, not the reproducibility of the training study.
- ✗
Increase the number of GPUs in every run so that throughput is maximized.
Why it's wrong here
Maximizing GPU count changes the effective global batch size and gradient accumulation behavior, which can alter convergence independently of the learning-rate schedule. It is an optimization choice, not a reproducibility requirement, and applying it inconsistently across runs would confound the comparison rather than make the experiments repeatable.
- ✓
Record the exact dataset version, tokenizer configuration, and NeMo container image tag used.
Why this is correct
Capturing dataset version, tokenizer settings, and container image tag pins the environment so a later engineer can rebuild the identical setup. Tokenizer changes alter token counts and therefore effective sequence lengths, while a different container may ship different library versions. Without these records, the learning-rate comparison cannot be faithfully repeated next quarter.
- ✗
Enable mixed precision training to reduce memory usage on each DGX node.
Why it's wrong here
Mixed precision affects numerical behavior and memory footprint but is a performance and efficiency setting, not a guarantee of reproducibility. If one run uses FP16 and another uses FP32, results may diverge for reasons unrelated to the learning-rate schedule. Consistency of precision matters, but enabling it is not itself a required reproducibility practice.
- ✓
Seed all random number generators, including data shuffling, dropout, and weight initialization.
Why this is correct
Seeding every stochastic source ensures that data order, dropout masks, and initial weights are identical across runs, which is essential when comparing two learning-rate schedules. Without consistent seeds, observed differences could come from randomness rather than the schedule, invalidating the comparison and preventing another engineer from reproducing the same result.
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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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