NCA-GENL Experimentation Practice Question
A research team is using NVIDIA NeMo to experiment with a large language model for a summarization task. They observe that the model's ROUGE scores vary significantly across different runs even when using the same hyperparameters and dataset. They suspect that non-deterministic operations in the training pipeline are causing this variance. Which step should they take to improve reproducibility of their experiment results?
⚠ Common exam trap
The trap here is thinking that statistical averaging or hyperparameter tuning can fix irreproducibility, when the real issue is non-deterministic operations that must be explicitly controlled.
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 algorithms and set a fixed random seed in the NeMo training configuration.
To achieve reproducibility, the team must eliminate non-deterministic sources. Setting a fixed random seed and enabling deterministic algorithms ensures that random number generation, data shuffling, and GPU operations are consistent across runs. This directly reduces variance in ROUGE scores and allows other researchers to replicate the experiment exactly, which is a cornerstone of rigorous LLM experimentation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the experiment multiple times and average the ROUGE scores to report a mean value.
Why it's wrong here
Averaging results across runs masks variance rather than eliminating it. While it provides a central tendency, it does not make the experiment reproducible; individual runs remain non-deterministic. For rigorous experimentation, the goal is to control randomness so that any run yields the same result. Averaging is a statistical workaround, not a solution to non-determinism.
- ✓
Enable deterministic algorithms and set a fixed random seed in the NeMo training configuration.
Why this is correct
Enabling deterministic algorithms and fixing the random seed ensures that operations like weight initialization, dropout, and data shuffling produce identical results across runs. This directly addresses the observed variance by eliminating non-deterministic sources. NeMo supports these settings, making it the correct approach to achieve reproducible experiments in summarization tasks.
- ✗
Use a different optimizer with adaptive learning rates to stabilize training.
Why it's wrong here
Switching optimizers may change convergence behavior but does not enforce determinism. Non-deterministic operations such as atomic adds on GPUs can still cause run-to-run variance. The team's issue is reproducibility, not optimization stability. Changing the optimizer without addressing randomness will not yield consistent ROUGE scores across identical runs.
- ✗
Increase the batch size to reduce the number of gradient updates per epoch.
Why it's wrong here
Increasing batch size changes the optimization dynamics and may affect convergence, but it does not eliminate non-deterministic operations. The variance across runs would likely persist because random seeds and non-deterministic kernels still introduce variability. This action does not target the root cause of irreproducibility and could even alter the model's performance characteristics.
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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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