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.
Refer to the exhibit. temperature: 0.7 top_p: 0.9 max_tokens: 50 frequency_penalty: 0 presence_penalty: 0
You are testing an Azure OpenAI model with the parameters shown in the exhibit. The model generates very short responses. Which parameter should you modify to allow longer responses?
Refer to the exhibit. temperature: 0.7 top_p: 0.9 max_tokens: 50 frequency_penalty: 0 presence_penalty: 0
Increase frequency_penalty
Why wrong: frequency_penalty reduces repetition, not length.
Increase top_p
Why wrong: top_p does not affect length.
Increase temperature
Why wrong: Temperature does not affect length.
Increase max_tokens
max_tokens directly controls the maximum length of the response.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase max_tokens
The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When responses are very short, increasing max_tokens allows the model to produce longer completions up to the specified limit. The other parameters affect randomness, diversity, or probability distribution, not the length cap.
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 frequency_penalty
Why it's wrong here
frequency_penalty reduces repetition, not length.
Increase top_p
Why it's wrong here
top_p does not affect length.
Increase temperature
Why it's wrong here
Temperature does not affect length.
Increase max_tokens
Why this is correct
max_tokens directly controls the maximum length of the response.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse parameters that control output length (max_tokens) with those that control output diversity or creativity (temperature, top_p, frequency_penalty), leading them to incorrectly adjust the latter when the real issue is a token limit.
Detailed technical explanation
Under the hood, max_tokens acts as a hard stop: once the model generates that many tokens, the response is truncated regardless of whether the model would continue. In contrast, parameters like frequency_penalty and presence_penalty modify logit scores during sampling to reduce repetition, but they do not extend the generation budget. A real-world scenario is a chatbot that needs to output detailed summaries; without increasing max_tokens, the model may cut off mid-sentence even if it has more to say.
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: Increase max_tokens — The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When responses are very short, increasing max_tokens allows the model to produce longer completions up to the specified limit. The other parameters affect randomness, diversity, or probability distribution, not the length cap.
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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