NCA-GENL Experimentation Practice Question
A data scientist wants to determine how sensitive an LLM's summarization quality is to the temperature sampling parameter. They plan a sweep across several temperature values. Which experimental approach gives the clearest signal about temperature's effect?
⚠ Common exam trap
The trap here is treating a faster joint sweep of temperature and prompt as equivalent, when it removes the ability to attribute results to temperature alone.
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
✓
Vary temperature while keeping the model, prompts, and evaluation metric constant.
To measure sensitivity to a single parameter, that parameter must be the only thing changing. Fixing the model, prompts, and evaluation metric ensures any quality difference across the sweep comes from temperature. This controlled setup produces a curve that shows whether summarization quality is robust or fragile with respect to the sampling temperature setting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different evaluation metric for each temperature value to capture more aspects of quality.
Why it's wrong here
Switching metrics across conditions makes scores incomparable, since each metric measures something different on a different scale. Apparent changes could reflect the metric rather than temperature. A valid sensitivity sweep uses one consistent metric so the values can be plotted and interpreted as a single curve.
- ✗
Change temperature and the prompt template together to explore the joint space faster.
Why it's wrong here
Altering temperature and the prompt simultaneously confounds the outcome: a quality shift could come from either change or their interaction. The experiment would not isolate temperature sensitivity, which is the stated goal. Controlling one variable at a time is necessary to draw a defensible conclusion about temperature.
- ✗
Test only the lowest and highest temperature values to save compute.
Why it's wrong here
Sampling only the extremes can miss non-monotonic behavior, such as a mid-range optimum where quality peaks. It also gives no information about the shape of the response, which is exactly what a sensitivity study should reveal. A broader sweep with a fixed setup provides a more informative and reliable conclusion.
- ✓
Vary temperature while keeping the model, prompts, and evaluation metric constant.
Why this is correct
Temperature is the factor under test, so isolating it by holding the model, prompts, and metric fixed lets any quality change be attributed to sampling temperature. This is the direct way to measure sensitivity. The sweep then reveals whether quality is stable, improves, or degrades across the temperature range.
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.