NCA-GENL Experimentation Practice Question
When designing an experiment to evaluate the performance of an LLM on a downstream classification task, which THREE factors should be controlled to ensure the results are comparable across different model sizes?
⚠ Common exam trap
Candidates often overlook inference-time parameters like temperature and top-p, assuming that only the dataset and model weights need to remain identical during comparative evaluations.
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
✓
Decoding strategy (temperature, top-p)
To conduct valid cross-model comparisons, one must neutralize external variables. Inference-time settings like decoding parameters, the specific prompt template, and the evaluation dataset must remain constant. If these vary, the results reflect differences in the evaluation environment rather than the intrinsic capabilities of the models being tested, rendering the experimentation data inconclusive for determining which model size is truly optimal for the specific classification application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Decoding strategy (temperature, top-p)
Why this is correct
The decoding strategy directly influences the stochastic nature of the output. If one model uses greedy search and another uses high-temperature sampling, the performance differences are skewed by the sampling method. Consistency here is essential to isolate the model's architectural capacity from its generation behavior during the experiment.
- ✓
The prompt template used
Why this is correct
Prompts are the interface between the user and the model. Different templates can lead to drastically different performance levels due to varying sensitivities in the model's pre-training. Standardizing the prompt ensures that every model size is evaluated under identical conditions, providing a fair basis for comparison across your experiments.
- ✗
The learning rate used during fine-tuning
Why it's wrong here
Learning rates are typically tuned per model size because different architectures have unique optimal convergence rates. Enforcing the exact same learning rate across vastly different model sizes would be detrimental, as the smaller model might underfit while the larger one might diverge, leading to an unfair experimental comparison.
- ✓
The validation/test dataset
Why this is correct
Using a consistent evaluation dataset is the baseline for any comparative experiment. If models are tested on different data splits, you cannot determine if a performance difference is due to the model's capabilities or the difficulty level of the test samples. This is critical for scientific integrity in evaluation.
- ✗
The hardware GPU generation (e.g., A100 vs H100)
Why it's wrong here
Hardware generation influences throughput and latency, but it does not change the model's generative logic or classification accuracy. As long as the precision is consistent, the model's output should remain identical regardless of the underlying GPU architecture, making this factor unnecessary to control for accuracy metrics.
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.