Max Tokens Parameter in Azure OpenAI
A developer uses Azure OpenAI to generate Python code snippets. They want to prevent the model from producing overly long and complex functions by setting a maximum length for the generated output. Which parameter should the developer set in the API call?
Quick Answer
The answer is the max_tokens parameter. This parameter directly controls the maximum number of tokens—which can be words, subwords, or code elements—that the Azure OpenAI model is allowed to generate in a single response. By setting a lower max_tokens value, the developer effectively caps the length of the generated Python code, preventing the model from producing overly long and complex functions. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control output length in generative AI models, often appearing in scenarios involving code generation or text summarization. A common trap is confusing max_tokens with temperature (which controls randomness) or top_p (which controls nucleus sampling), so remember that max_tokens is purely about length, not creativity. A helpful memory tip: think of max_tokens as a “word limit” for the model’s reply—just like a character count in a text message.
⚠ Common exam trap
Test-takers frequently confuse `max_tokens` with `temperature` or `top_p`, thinking those parameters control output length, when in fact they only affect the randomness or diversity of the generated text.
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
✓
max_tokens
The `max_tokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting a lower `max_tokens` value, the developer caps the length of the generated Python code, preventing overly long and complex functions. This directly addresses the requirement to limit output 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.
- ✗
temperature
Why it's wrong here
temperature rescales the probability distribution, altering randomness rather than the number of tokens emitted, so a low value still permits long functions. It is tempting because lower temperature yields more predictable text, which suits deterministic code generation, but the stem requires max_tokens, which enforces the output length limit.
- ✗
top_p
Why it's wrong here
top_p narrows sampling to a cumulative probability mass, changing which tokens are considered rather than how many are produced, so output length remains unbounded. It is tempting because restricting the token pool appears to constrain responses, which suits controlling diversity, but the stem requires max_tokens to cap generated length.
- ✓
max_tokens
Why this is correct
max_tokens caps the number of tokens the model generates in its completion, directly limiting output length. Setting it prevents excessively long, complex functions, satisfying the developer's requirement for a maximum generated output length in the Azure OpenAI API call.
- ✗
frequency_penalty
Why it's wrong here
frequency_penalty discourages repeated tokens, so it curbs looping phrases but cannot cap output length; a verbose function still generates until it stops naturally. It is tempting because penalising repetition appears to shorten text, which suits reducing verbatim echoing, but the stem requires max_tokens, which truncates generation at a fixed token count.
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1 more way this is tested on AI-900
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Variation 1. A developer uses Azure OpenAI to generate Python code. They want the model to limit the length of the generated code to avoid overly long and complex functions. Which parameter should the developer set in the API call?
medium- A.temperature
- ✓ B.max_tokens
- C.top_p
- D.frequency_penalty
Why B: The `max_tokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting a lower `max_tokens` value, the developer can cap the length of the generated Python code, preventing overly long and complex functions. This is the correct parameter for limiting output length.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.