The answer is selecting a smaller model like GPT-3.5-turbo for simpler tasks and setting the max_tokens parameter to the minimum needed for the response. These two actions directly manage cost in Azure OpenAI Service because pricing is based on both the model tier and the number of tokens processed per request. Using a less expensive model for low-complexity tasks reduces per-token charges, while capping max_tokens prevents the model from generating unnecessary output that inflates your bill. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of operational cost optimization within Azure OpenAI, often appearing as a multiple-select item where a common trap is choosing “reducing the number of API calls” instead of controlling token limits. Remember the mnemonic “Model and Max” — pick the right model for the job and cap the tokens to cap the cost.
AI-102 Implement generative AI solutions Practice Question
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.
Which TWO are valid ways to manage cost when using Azure OpenAI Service in a production application?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a smaller model like GPT-3.5-turbo instead of GPT-4 for simpler tasks
Option C is correct because using a smaller model like GPT-3.5-turbo for simpler tasks directly reduces the per-token cost compared to GPT-4, which is significantly more expensive. Azure OpenAI Service charges based on model tier and token usage, so selecting the appropriate model for the task complexity is a primary cost management strategy.
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.
✗
Fine-tune the model to reduce the number of examples needed in prompts
Why it's wrong here
Fine-tuning has initial cost and may not reduce overall token usage.
✗
Increase the temperature parameter to 1.0
Why it's wrong here
Temperature does not affect token count or cost.
✓
Use a smaller model like GPT-3.5-turbo instead of GPT-4 for simpler tasks
Why this is correct
Smaller models have lower per-token costs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Provision more PTUs to get a lower rate per token
Why it's wrong here
PTU is a fixed monthly cost, not variable; may increase cost.
✓
Set the max_tokens parameter to the minimum needed for the response
Why this is correct
Reduces token usage per request.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse fine-tuning with prompt optimization, or assume that increasing PTUs lowers per-token cost, when in fact PTUs are a fixed-cost commitment that increases total expenditure.
Detailed technical explanation
How to think about this question
Azure OpenAI Service pricing is model-specific and token-based, with GPT-4 costing approximately 20x more per token than GPT-3.5-turbo. The max_tokens parameter directly controls the length of the generated response, and setting it to the minimum needed prevents unnecessary token consumption, which is a direct cost control mechanism. Under the hood, each API call bills for both prompt and completion tokens, so reducing max_tokens reduces the completion token count and thus the cost.
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 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.
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: Use a smaller model like GPT-3.5-turbo instead of GPT-4 for simpler tasks — Option C is correct because using a smaller model like GPT-3.5-turbo for simpler tasks directly reduces the per-token cost compared to GPT-4, which is significantly more expensive. Azure OpenAI Service charges based on model tier and token usage, so selecting the appropriate model for the task complexity is a primary cost management strategy.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO options are valid ways to reduce the cost of using Azure OpenAI Service?
medium
✓ A.Use provisioned throughput with reserved capacity.
B.Increase the temperature parameter.
✓ C.Use a smaller model like GPT-3.5 instead of GPT-4.
D.Increase the max_tokens parameter to get longer responses.
E.Enable content filtering on all requests.
Why A: Options A and D are correct. A: Using a smaller model like GPT-3.5 instead of GPT-4 reduces cost per token. D: Provisioned throughput with reserved capacity offers lower per-token cost for high usage. B is wrong because increasing max_tokens increases cost. C is wrong because using a higher temperature does not affect token count. E is wrong because content filters do not reduce token consumption.
Last reviewed: Jun 24, 2026
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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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