Quick Answer
The answer is to reduce the max_tokens parameter in the API request. This works because Azure OpenAI generates responses token by token in an autoregressive fashion, so capping the maximum number of output tokens directly shortens the number of sequential decoding steps the model must perform, which reduces processing time and lowers latency without altering the underlying model. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of how API parameters affect performance versus model architecture—a common trap is to assume you need to switch to a smaller model or adjust temperature, but the key is that max_tokens controls response length, not quality. Remember the memory tip: “Max tokens maxes out the wait—shrink the cap to speed the lap.”
This AI-102 practice question tests your understanding of implement generative ai solutions. 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.
You are developing a generative AI application that uses Azure OpenAI Service to summarize large documents. The application experiences high latency when processing requests. You need to reduce the latency without changing the model. What should you do?
Increase the temperature parameter
Why wrong: Increasing temperature does not reduce latency.
Increase the top_p parameter
Why wrong: Increasing top_p does not reduce latency.
Reduce the max_tokens parameter in the API request
Reducing max_tokens limits output length, reducing processing time.
Increase the max_tokens parameter
Why wrong: Increasing max_tokens would increase latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Reduce the max_tokens parameter in the API request
Reducing the max_tokens parameter limits the length of the generated response, which directly reduces the processing time required by the Azure OpenAI Service to produce the output. Since latency is caused by the model generating a long sequence of tokens, capping the output tokens decreases the number of autoregressive decoding steps, thereby lowering response time without altering the underlying model.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the temperature parameter
Why it's wrong here
Increasing temperature does not reduce latency.
Increase the top_p parameter
Why it's wrong here
Increasing top_p does not reduce latency.
Reduce the max_tokens parameter in the API request
Why this is correct
Reducing max_tokens limits output length, reducing processing time.
Related concept
Read the scenario before looking for a memorised answer.
Increase the max_tokens parameter
Why it's wrong here
Increasing max_tokens would increase latency.
Common exam traps
The trap here is that candidates often confuse parameters that affect output length (max_tokens) with those that affect output diversity (temperature, top_p), mistakenly believing that adjusting randomness can speed up generation.
Detailed technical explanation
The max_tokens parameter in Azure OpenAI API requests sets the maximum number of tokens (both input and output) the model can generate in a single completion. Under the hood, the model performs autoregressive token generation, where each new token depends on previous ones; reducing max_tokens cuts the number of sequential decoding iterations, directly lowering the total inference time. In real-world scenarios, for summarization tasks, setting max_tokens to a value just above the expected summary length (e.g., 200 tokens for a one-paragraph summary) can significantly reduce latency while still capturing key information.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the max_tokens parameter in the API request — Reducing the max_tokens parameter limits the length of the generated response, which directly reduces the processing time required by the Azure OpenAI Service to produce the output. Since latency is caused by the model generating a long sequence of tokens, capping the output tokens decreases the number of autoregressive decoding steps, thereby lowering response time without altering the underlying model.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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