NCA-GENL Experimentation Practice Question
A researcher is experimenting with prompt-tuning and finds that the model output is repetitive. They decide to adjust the sampling hyperparameters. Which combination of changes is most likely to increase the diversity of the output?
⚠ Common exam trap
Test-takers frequently confuse parameter directions, accidentally suggesting decreases in temperature or top-p when trying to fix repetitive and deterministic model outputs.
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
✓
Increase temperature and increase top-p.
Sampling parameters control the trade-off between coherence and creativity. Increasing temperature shifts the probability distribution, allowing for less likely tokens to be selected, while increasing top-p (nucleus sampling) expands the set of tokens considered. Balancing these allows researchers to explore the model's creative range during experimentation, ensuring the output is varied enough to be useful while maintaining sufficient logical coherence for the application's specific requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease temperature and decrease top-p.
Why it's wrong here
Lowering both temperature and top-p restricts the model to only the most likely tokens. This makes the generation highly deterministic and repetitive, which is the exact opposite of the desired goal. This configuration is typically used when seeking factual precision rather than linguistic diversity or creative variation.
- ✓
Increase temperature and increase top-p.
Why this is correct
Higher temperature flattens the probability distribution, increasing the chance of picking diverse tokens. Increasing top-p broadens the cumulative probability mass considered during sampling. Together, these settings allow the model to select from a wider vocabulary, effectively reducing repetitive output patterns during the experimentation phase of model evaluation.
- ✗
Set temperature to 0 and top-p to 1.
Why it's wrong here
Setting temperature to 0 forces greedy decoding, where the model always chooses the highest-probability token. This results in the most repetitive output possible, as the model will always follow the same path for a given prompt, completely ignoring the potential for linguistic diversity or stylistic variation.
- ✗
Increase frequency penalty and decrease top-p.
Why it's wrong here
While increasing the frequency penalty discourages repetition, decreasing top-p narrows the selection window to the most probable tokens. This combination often leads to disjointed or grammatically incorrect text because the model is forced to avoid frequent tokens but is not given access to a wide enough vocabulary.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.