1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A developer notices that an LLM's responses are too verbose. Which parameter adjustment would most effectively reduce verbosity?
⚠ Common exam trap
Test-takers frequently confuse parameters that affect output style (temperature, top_p, frequency_penalty) with the one that directly controls output length (max_tokens), leading them to choose a parameter that changes how the model says something rather than how much it says.
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
✓
Decrease max_tokens
Decreasing max_tokens directly limits the maximum length of the LLM's response, which is the most straightforward way to reduce verbosity. This parameter caps the number of tokens the model can generate, forcing it to produce shorter completions. Other parameters like frequency_penalty, top_p, and temperature influence the style, diversity, or randomness of the output but do not directly control response length.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase frequency_penalty
Why it's wrong here
Frequency penalty reduces token repetition but not overall length.
- ✗
Increase top_p
Why it's wrong here
Increasing top_p allows more token sampling, potentially increasing verbosity.
- ✓
Decrease max_tokens
Why this is correct
Max_tokens directly controls the maximum output length, reducing verbosity.
- ✗
Decrease temperature
Why it's wrong here
Lower temperature makes output more focused but does not directly limit length.
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