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 using Azure OpenAI Service to generate product descriptions. The output is often too verbose. You need to reduce the length of generated text without changing the model. Which parameter should you adjust?
Max tokens
Max tokens limits the length of the generated response.
Frequency penalty
Why wrong: Frequency penalty reduces word repetition, not length.
Temperature
Why wrong: Temperature affects randomness, not response length.
Top-p (nucleus sampling)
Why wrong: Top-p affects the probability mass considered, not length.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Max tokens
Max tokens controls the total length of the generated response by capping the number of tokens (words/subwords) the model can output. Reducing this value directly truncates the output, making descriptions shorter without altering the model or its behavior. Other parameters influence randomness or repetition but do not enforce a strict length limit.
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.
Max tokens
Why this is correct
Max tokens limits the length of the generated response.
Related concept
Read the scenario before looking for a memorised answer.
Frequency penalty
Why it's wrong here
Frequency penalty reduces word repetition, not length.
Temperature
Why it's wrong here
Temperature affects randomness, not response length.
Top-p (nucleus sampling)
Why it's wrong here
Top-p affects the probability mass considered, not length.
Common exam traps
The trap here is that candidates confuse parameters that affect output style (temperature, top-p, frequency penalty) with the one parameter that directly controls output length (max tokens), leading them to choose a parameter that changes how the model writes rather than how much it writes.
Detailed technical explanation
Under the hood, the max_tokens parameter in the Azure OpenAI API sets an absolute upper bound on the number of tokens (including both prompt and completion) the model can generate. Once this limit is reached, generation stops immediately, even if the model would naturally continue. In real-world scenarios, setting max_tokens too low can cut off sentences mid-word, so it's often combined with a stop sequence or careful prompt engineering to ensure complete outputs.
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: Max tokens — Max tokens controls the total length of the generated response by capping the number of tokens (words/subwords) the model can output. Reducing this value directly truncates the output, making descriptions shorter without altering the model or its behavior. Other parameters influence randomness or repetition but do not enforce a strict length limit.
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
This AI-102 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-102 exam.
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