Quick Answer
The answer is to decrease the max_tokens parameter. This adjustment directly caps the maximum length of the generated response, limiting the number of tokens the model can produce and forcing it to output shorter, less verbose completions. While parameters like temperature, top_p, or frequency_penalty influence creativity, randomness, or repetition, they do not control the overall response length. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding of how each parameter uniquely shapes output, with max_tokens being the only one that sets a hard boundary on verbosity. A common trap is confusing verbosity with repetition or style; remember that verbosity is about length, not content diversity. For a quick memory tip, think of max_tokens as a “word limit” for the LLM—just like a strict character count in a text box, it forces the model to get to the point.
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer notices that an LLM's responses are too verbose. Which parameter adjustment would most effectively reduce verbosity?
Increase frequency_penalty
Why wrong: Frequency penalty reduces token repetition but not overall length.
Increase top_p
Why wrong: Increasing top_p allows more token sampling, potentially increasing verbosity.
Decrease max_tokens
Max_tokens directly controls the maximum output length, reducing verbosity.
Decrease temperature
Why wrong: Lower temperature makes output more focused but does not directly limit length.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Decrease temperature
Why it's wrong here
Lower temperature makes output more focused but does not directly limit length.
Common exam traps
The trap here is that candidates 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.
Trap categories for this question
Command / output trap
Lower temperature makes output more focused but does not directly limit length.
Detailed technical explanation
The max_tokens parameter sets an absolute upper bound on the number of tokens (words or subwords) in the generated output. Under the hood, the model's decoding loop stops when either the end-of-sequence token is generated or the max_tokens limit is reached. In real-world scenarios, such as API calls to GPT-4 or Claude, reducing max_tokens is essential for cost control and latency reduction, especially when the use case requires concise answers like code snippets or summaries.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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