AI-102 Implement generative AI solutions Practice Question
A research lab wants to use Azure OpenAI to generate synthetic data for training a model. They need to generate a large volume of data quickly and cost-effectively. Which approach should they use?
⚠ Common exam trap
Many exam-takers confuse the batch API with the streaming API, assuming streaming is faster for volume, but the batch API is specifically designed for high-throughput, cost-effective asynchronous processing, not real-time use.
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 the batch API to process requests asynchronously.
The batch API is designed for high-throughput, asynchronous processing of large volumes of requests, making it ideal for generating synthetic data at scale. It allows the lab to submit many prompts in a single batch, which Azure OpenAI processes efficiently, reducing both cost and time compared to real-time processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the batch API to process requests asynchronously.
Why this is correct
Batch API is designed for high volume with lower cost.
- ✗
Fine-tune a model to generate the data locally.
Why it's wrong here
Fine-tuning is for customization, not data generation.
- ✗
Use the streaming API with multiple concurrent connections.
Why it's wrong here
Streaming is for real-time, not bulk generation.
- ✗
Deploy a model on Azure Functions and call it in parallel.
Why it's wrong here
Azure Functions adds overhead and complexity.
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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.