Question 648 of 1,020

Describe features of generative AI workloads on Azure →easyMultiple ChoiceObjective-mapped

What Is the Max Tokens Parameter in Azure OpenAI?

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.

What is 'max_tokens' parameter in Azure OpenAI and how does it affect responses?

A
The maximum number of tokens in the input prompt the model can process
Why wrong: Input limit is part of the context window — max_tokens specifically controls the maximum length of the model's generated response.
B
A limit on the model's generated response length — stopping output at the specified token count
max_tokens caps output length — preventing runaway long responses and controlling costs by limiting generated token count.
C
The total number of API calls allowed per Azure subscription per hour
Why wrong: API call limits are rate limits — max_tokens is a per-request parameter controlling response length.
D
The maximum number of conversation turns before the session resets
Why wrong: Conversation turn limits are application design choices — max_tokens is about the token length of individual model responses.

Full breakdown with real-world context →

Quick Answer

The correct answer is that the max_tokens parameter in Azure OpenAI sets a hard limit on the number of tokens the model can generate in its response, stopping output once that count is reached. This parameter directly controls response length by capping the completion tokens—words or subwords—the model produces, which is distinct from input token limits that apply to the prompt you send. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to manage and constrain generative model outputs, often appearing in scenarios where you need to prevent overly long or rambling answers. A common trap is confusing max_tokens with the total token limit for the entire request (input plus output), but remember that max_tokens only governs the generated response. For a quick memory tip, think of it as a “maximum ceiling” for the model’s reply: once the ceiling is hit, the conversation stops.

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

✓

A limit on the model's generated response length — stopping output at the specified token count

Option B is correct because the 'max_tokens' parameter in Azure OpenAI sets a hard limit on the number of tokens (words or subwords) the model can generate in its response. Once this token count is reached, the model stops producing further output, effectively controlling response length. This is distinct from input processing limits, as 'max_tokens' applies solely to the generated completion.

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.

✗
The maximum number of tokens in the input prompt the model can process
Why it's wrong here
Input limit is part of the context window — max_tokens specifically controls the maximum length of the model's generated response.
✓
A limit on the model's generated response length — stopping output at the specified token count
Why this is correct
max_tokens caps output length — preventing runaway long responses and controlling costs by limiting generated token count.
Related concept
Read the scenario before looking for a memorised answer.
✗
The total number of API calls allowed per Azure subscription per hour
Why it's wrong here
API call limits are rate limits — max_tokens is a per-request parameter controlling response length.
✗
The maximum number of conversation turns before the session resets
Why it's wrong here
Conversation turn limits are application design choices — max_tokens is about the token length of individual model responses.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is confusing 'max_tokens' with the model's total context window limit, leading candidates to mistakenly think it caps the input prompt length instead of the output generation.

Detailed technical explanation

How to think about this question

Under the hood, tokens are subword units determined by the model's tokenizer (e.g., GPT uses Byte-Pair Encoding). The 'max_tokens' parameter interacts with the model's total context window: the sum of prompt tokens and 'max_tokens' must not exceed the model's maximum context length (e.g., 4096 for GPT-3.5-Turbo). In a real-world scenario, setting 'max_tokens' too low can truncate a response mid-sentence, while setting it too high may waste quota or exceed the context window, causing an error.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

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: A limit on the model's generated response length — stopping output at the specified token count — Option B is correct because the 'max_tokens' parameter in Azure OpenAI sets a hard limit on the number of tokens (words or subwords) the model can generate in its response. Once this token count is reached, the model stops producing further output, effectively controlling response length. This is distinct from input processing limits, as 'max_tokens' applies solely to the generated completion.

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.

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