Question 427 of 1,020

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

How the Max Tokens Parameter Controls Output Length and Cost 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 the maximum output length parameter 'max tokens' used for in Azure OpenAI?

A
The maximum number of API requests per second
Why wrong: API rate limits are a separate parameter — max_tokens controls the length of the generated response.
B
The maximum number of tokens in the generated response to control length and cost
max_tokens caps the output length — shorter max means faster, cheaper responses; too short may truncate answers.
C
The maximum number of words in the input prompt
Why wrong: Input prompt length is limited by the context window, not max_tokens — max_tokens controls output generation length.
D
The maximum number of concurrent users of the model
Why wrong: Concurrency is managed through API rate limits and deployments — max_tokens is a per-request output length parameter.

Full breakdown with real-world context →

Quick Answer

The correct answer is that the max tokens parameter controls the maximum number of tokens in the generated response to limit both output length and cost. This works because Azure OpenAI charges per token, and each token represents roughly 0.75 words of text; by capping the token count, you directly prevent the model from producing overly long or expensive completions. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how to manage resource consumption and response size in Azure OpenAI services, often appearing in scenario-based questions where you must choose the right parameter to control spending. A common trap is confusing max tokens with temperature or top-p, which affect creativity rather than length. Remember the memory tip: “Max tokens = max money and max words,” so setting it low saves both cost and verbosity.

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

✓

The maximum number of tokens in the generated response to control length and cost

The 'max tokens' parameter in Azure OpenAI controls the maximum number of tokens (roughly 0.75 words per token) that the model can generate in a single response. This directly limits the length of the output, which in turn controls both the cost (since Azure OpenAI charges per token) and the response size, preventing excessively long or expensive completions.

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 API requests per second
Why it's wrong here
API rate limits are a separate parameter — max_tokens controls the length of the generated response.
✓
The maximum number of tokens in the generated response to control length and cost
Why this is correct
max_tokens caps the output length — shorter max means faster, cheaper responses; too short may truncate answers.
Related concept
Read the scenario before looking for a memorised answer.
✗
The maximum number of words in the input prompt
Why it's wrong here
Input prompt length is limited by the context window, not max_tokens — max_tokens controls output generation length.
✗
The maximum number of concurrent users of the model
Why it's wrong here
Concurrency is managed through API rate limits and deployments — max_tokens is a per-request output length parameter.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse 'max tokens' with input length limits or rate limits, because the term 'maximum' sounds like a general cap, but it specifically applies only to the generated response tokens, not to the prompt or API throughput.

Trap categories for this question

Command / output trap
Input prompt length is limited by the context window, not max_tokens — max_tokens controls output generation length.

Detailed technical explanation

How to think about this question

Under the hood, each token is a subword unit; for example, 'Azure OpenAI' might be tokenized as ['Azure', 'Open', 'AI']. The 'max tokens' parameter acts as a hard stop: once the model generates that many tokens, it truncates the response, even if the model would naturally continue. In a real-world scenario, setting 'max tokens' too low can cut off a critical sentence in a summarization task, while setting it too high may lead to unnecessary cost if the model rambles. The total tokens consumed (input + output) also counts against the model's context window limit, so careful tuning is essential for both cost and completeness.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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: The maximum number of tokens in the generated response to control length and cost — The 'max tokens' parameter in Azure OpenAI controls the maximum number of tokens (roughly 0.75 words per token) that the model can generate in a single response. This directly limits the length of the output, which in turn controls both the cost (since Azure OpenAI charges per token) and the response size, preventing excessively long or expensive completions.

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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