NCA-GENL Experimentation Practice Question
A data scientist is experimenting with an LLM for a text generation task using NVIDIA NeMo. They want to measure how the model's output diversity changes when adjusting the temperature parameter. They plan to generate 100 samples for each temperature setting and compute the distinct-n metric. Which experimental design principle are they applying?
⚠ Common exam trap
Test-takers frequently confuse controlled experimentation with hyperparameter optimization; the former seeks to understand effects, while the latter seeks to find optimal values.
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
✓
Controlled experiment
The scientist is manipulating a single independent variable (temperature) while holding other factors constant, and measuring its effect on a dependent variable (output diversity via distinct-n). This systematic approach is a controlled experiment, which allows for causal inference about the relationship between temperature and diversity. It is a fundamental design principle in 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.
- ✗
Ablation study
Why it's wrong here
An ablation study involves removing or altering components of a model to understand their contribution. Here, the scientist is not removing any component but rather varying a single hyperparameter to observe its effect on output diversity. Ablation would be more appropriate for testing the impact of, say, removing a layer or a feature, not for tuning a sampling parameter.
- ✗
Cross-validation
Why it's wrong here
Cross-validation is a technique for assessing how a model generalizes to an independent dataset by partitioning data into folds. It is not applicable here because the scientist is not evaluating model generalization across different data splits but rather observing how a generation parameter affects output diversity on a fixed prompt set. The scenario lacks any data partitioning or fold-based evaluation.
- ✗
Hyperparameter optimization
Why it's wrong here
Hyperparameter optimization aims to find the best hyperparameter values according to a target metric. While temperature is a hyperparameter, the scientist's goal is to measure its effect on diversity, not to optimize it for a specific objective. This is an exploratory analysis rather than an optimization process, which would involve search algorithms like grid or Bayesian optimization.
- ✓
Controlled experiment
Why this is correct
By varying only the temperature while keeping other factors constant, the scientist is conducting a controlled experiment. This design isolates the effect of temperature on output diversity, allowing valid conclusions. Generating multiple samples and computing distinct-n provides a quantitative measure, which is characteristic of controlled experimentation in LLM evaluation.
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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
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.