The correct answer is to decrease max_tokens to 100 because this parameter directly caps the length of the generated output, forcing the model to produce a more concise summary. Unlike parameters such as temperature or top_p, which control creativity or randomness, max_tokens strictly limits the number of tokens the model can generate, so reducing it from a higher value to 100 truncates verbose responses while preserving factual accuracy—the model still selects the most relevant content within the shorter space. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of how to make Azure OpenAI summaries concise with max_tokens, a common scenario in document summarization tasks where you must balance brevity against completeness. A frequent trap is confusing max_tokens with frequency penalty or thinking it alters content selection; remember that max_tokens is a hard length limit, not a quality filter. For a quick memory tip: think of max_tokens as a word count cap—lower it to tighten the output, just like setting a strict character limit for a tweet.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Exhibit
{
"completions": [
{
"prompt": "Generate a summary of the following text: ...",
"parameters": {
"temperature": 0.3,
"max_tokens": 150,
"top_p": 0.9,
"frequency_penalty": 0.0,
"presence_penalty": 0.0
},
"response": "..."
}
]
}
Refer to the exhibit. You are configuring an Azure OpenAI Service deployment for document summarization. The current parameters produce summaries that are often too verbose. You need to make the summaries more concise while maintaining factual accuracy. Which parameter change should you make?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease max_tokens to 100
Option C is correct because decreasing max_tokens to 100 directly limits the maximum length of the generated summary, forcing the model to produce shorter output. This addresses the verbosity issue without altering the model's factual accuracy, as max_tokens controls output length, not content selection or creativity.
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.
✗
Increase top_p to 1.0
Why it's wrong here
Increasing top_p may increase diversity but not conciseness.
✗
Increase frequency_penalty to 0.5
Why it's wrong here
Frequency penalty reduces repetition, not length.
✓
Decrease max_tokens to 100
Why this is correct
Reducing max_tokens limits output length, making summaries more concise.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase temperature to 0.7
Why it's wrong here
Increasing temperature increases randomness, not conciseness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between parameters that control output length (max_tokens) versus those that control creativity or diversity (temperature, top_p, frequency_penalty), leading candidates to mistakenly adjust the latter when the issue is simply excessive length.
Detailed technical explanation
How to think about this question
The max_tokens parameter in Azure OpenAI Service defines the maximum number of tokens (words or subwords) the model can generate in a single completion. Under the hood, the model stops generating once it reaches this limit or encounters a stop sequence, making it a hard cap on output length. In real-world scenarios, setting max_tokens too low can truncate important content, so it must be balanced with the desired summary conciseness while ensuring key facts are preserved.
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 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
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.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease max_tokens to 100 — Option C is correct because decreasing max_tokens to 100 directly limits the maximum length of the generated summary, forcing the model to produce shorter output. This addresses the verbosity issue without altering the model's factual accuracy, as max_tokens controls output length, not content selection or creativity.
What should I do if I get this AI-102 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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Question Discussion
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