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.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Trap categories for this question
Command / output trap
Frequency penalty reduces the likelihood of repeating the same tokens, but does not control the overall output length.
Detailed technical explanation
How to think about this question
Under the hood, `max_tokens` includes both the prompt tokens and the generated tokens; if the prompt is long, the model may stop early because the total exceeds the limit. In practice, setting `max_tokens` too low can truncate a function mid-syntax, so developers often combine it with a stop sequence (e.g., a newline or a specific string) to ensure clean code generation. This parameter is essential for cost control and latency management in production Azure OpenAI deployments.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
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.
Last reviewed: Jun 11, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.