AI-102 Implement generative AI solutions Practice Question
Which TWO are valid ways to manage cost when using Azure OpenAI Service in a production application?
⚠ Common exam trap
Many exam-takers 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.
Answer choices
Why each option matters
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
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.
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.
- ✗
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.
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Same concept, more angles
1 more way this is tested on AI-102
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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: Provisioned throughput with reserved capacity allows you to commit to a specific amount of throughput (tokens per minute) for a fixed period, typically one month or one year, in exchange for a significant discount compared to pay-as-you-go pricing. This reduces per-token costs when you have predictable workloads.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.