The correct parameter to adjust is max_tokens, because it directly limits the number of tokens the model can generate in a single response, thereby capping the output length and reducing the total tokens processed (input + output) to lower API costs. In Azure OpenAI, the cost is calculated per token, so setting a lower max_tokens value ensures the generated marketing copy stays brief, minimizing expenses while still delivering effective text. On the AI-900 exam, this concept tests your understanding of how API pricing works and which parameter controls output length—a common trap is confusing max_tokens with temperature or top_p, which affect creativity or diversity but do not limit response size. A helpful memory tip: think of max_tokens as a “budget cap” for the output; the lower the cap, the lower the cost.
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 Service to generate creative marketing copy. The API costs are based on the total number of tokens processed (input + output). To minimize costs, the developer wants to ensure that the generated text is as brief as possible while still being effective. Which parameter should the developer adjust in the API request?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Why wrong: Temperature controls the randomness of the output; lowering it makes the output more deterministic, but it does not affect the length of the generated text.
B
top_p
Why wrong: Top_p (nucleus sampling) controls the diversity of the output by considering only the most probable tokens, but it does not limit the number of tokens generated.
C
max_tokens
Max_tokens explicitly sets the upper limit on the number of tokens the model can produce in a single response. Reducing this value shortens the output and reduces token costs.
D
frequency_penalty
Why wrong: Frequency_penalty applies a penalty to tokens that have already appeared in the text, reducing repetition but not controlling the length of the generated content.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
max_tokens
Option C (max_tokens) is correct because this parameter directly controls the maximum number of tokens the model can generate in a single response. By setting a lower max_tokens value, the developer caps the length of the output, which reduces the total tokens processed (input + output) and thus lowers API costs. Other parameters influence the style or diversity of the output but do not directly limit the length of the generated text.
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 output; lowering it makes the output more deterministic, but it does not affect the length of the generated text.
✗
top_p
Why it's wrong here
Top_p (nucleus sampling) controls the diversity of the output by considering only the most probable tokens, but it does not limit the number of tokens generated.
✓
max_tokens
Why this is correct
Max_tokens explicitly sets the upper limit on the number of tokens the model can produce in a single response. Reducing this value shortens the output and reduces token costs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
frequency_penalty
Why it's wrong here
Frequency_penalty applies a penalty to tokens that have already appeared in the text, reducing repetition but not controlling the length of the generated content.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse parameters that affect output style (temperature, top_p, frequency_penalty) with the one that directly controls output length (max_tokens), leading them to pick a parameter that changes how the model writes rather than how much it writes.
Trap categories for this question
Command / output trap
Temperature controls the randomness of the output; lowering it makes the output more deterministic, but it does not affect the length of the generated text.
Detailed technical explanation
How to think about this question
Under the hood, max_tokens sets an absolute limit on the number of tokens (including spaces and punctuation) the model can output; once this limit is reached, generation stops even if the response is incomplete. In Azure OpenAI, tokens are counted for both the prompt and the completion, so a lower max_tokens directly reduces the completion portion. A real-world scenario is a chatbot that must stay under a cost budget per query—setting max_tokens to 50 ensures short replies and predictable pricing, whereas temperature or top_p adjustments would not guarantee brevity.
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.
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 — Option C (max_tokens) is correct because this parameter directly controls the maximum number of tokens the model can generate in a single response. By setting a lower max_tokens value, the developer caps the length of the output, which reduces the total tokens processed (input + output) and thus lowers API costs. Other parameters influence the style or diversity of the output but do not directly limit the length of the generated text.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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