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 controls the randomness of the generated text, not its length.
- ✗
top_p
Why it's wrong here
Top_p (nucleus sampling) controls the cumulative probability threshold for token selection, affecting diversity, not length.
- ✓
max_tokens
Why this is correct
max_tokens limits the total number of tokens (words/characters) generated by the model.
- ✗
frequency_penalty
Why it's wrong here
Frequency penalty reduces the likelihood of repeating the same tokens, but does not control the overall output length.
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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.