Quick Answer
The answer is to decrease the max_tokens parameter. This is correct because Azure OpenAI Service charges per token for both input and output, so capping the maximum number of tokens the model can generate directly reduces the compute cost per API call. By limiting response length, you prevent unnecessarily verbose output while preserving the model’s ability to produce high-quality, concise marketing copy. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of cost optimization strategies within Azure OpenAI, often appearing as a trap where candidates might mistakenly adjust temperature or top_p instead. Remember, temperature controls creativity, not length—max_tokens is the only parameter that directly controls output length and thus inference cost. A helpful memory tip: “Max tokens maxes out your bill, so shrink it to save.”
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 marketing copy. You have a requirement to reduce the cost of inference without significantly impacting output quality. Which parameter should you adjust?
Decrease the max_tokens.
Lower max_tokens limits the output length, reducing tokens consumed and cost.
Increase the frequency_penalty.
Why wrong: Frequency penalty discourages repetition but may not reduce overall tokens significantly.
Decrease the temperature.
Why wrong: Temperature affects randomness, not the number of tokens generated.
Increase the top_p.
Why wrong: Top_p controls nucleus sampling, does not reduce token count.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Decrease the max_tokens.
Decreasing max_tokens directly reduces the number of tokens generated per API call, which lowers the compute cost because Azure OpenAI charges per token (both input and output). Since the requirement is to reduce inference cost without significantly impacting output quality, reducing max_tokens is the most direct and effective parameter. It caps the response length, preventing unnecessarily verbose output while preserving the model's ability to generate high-quality, concise copy.
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.
Decrease the max_tokens.
Why this is correct
Lower max_tokens limits the output length, reducing tokens consumed and cost.
Related concept
Read the scenario before looking for a memorised answer.
Increase the frequency_penalty.
Why it's wrong here
Frequency penalty discourages repetition but may not reduce overall tokens significantly.
Decrease the temperature.
Why it's wrong here
Temperature affects randomness, not the number of tokens generated.
Increase the top_p.
Why it's wrong here
Top_p controls nucleus sampling, does not reduce token count.
Common exam traps
Microsoft often tests the misconception that temperature or top_p are cost-control parameters, when in fact they only affect output diversity and randomness, not token count or pricing; candidates mistakenly think lowering temperature reduces cost because it 'simplifies' output, but the real cost driver is token length.
Detailed technical explanation
Under the hood, Azure OpenAI's tokenizer counts both input and output tokens; each API call's cost is the sum of these tokens multiplied by the model's per-token rate. Reducing max_tokens caps the output token count, directly limiting the billable amount. In real-world scenarios, marketing copy often requires concise outputs (e.g., 50–100 tokens), so setting max_tokens to a low value like 150 ensures the model stops generating after reaching that limit, avoiding wasteful continuation. A subtle behavior is that max_tokens includes the entire response, including any stop sequences or special tokens, so setting it too low might truncate a useful response mid-sentence.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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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: Decrease the max_tokens. — Decreasing max_tokens directly reduces the number of tokens generated per API call, which lowers the compute cost because Azure OpenAI charges per token (both input and output). Since the requirement is to reduce inference cost without significantly impacting output quality, reducing max_tokens is the most direct and effective parameter. It caps the response length, preventing unnecessarily verbose output while preserving the model's ability to generate high-quality, concise copy.
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 30, 2026
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