- A
An API for training new models in batches on your custom datasets
Why wrong: Model training is fine-tuning — the batch API processes large volumes of inference requests asynchronously at lower cost.
- B
Asynchronous bulk processing of large inference request volumes at reduced cost
Batch API runs high-volume jobs (thousands of requests) asynchronously within 24h at ~50% cost reduction — ideal for offline processing.
- C
Grouping multiple Azure OpenAI API keys into a batch for easier management
Why wrong: API key management is Azure resource administration — the batch API is for bulk inference processing.
- D
A tool for running multiple prompt experiments simultaneously to find the best prompt
Why wrong: Prompt experimentation is evaluation/testing — the batch API processes production inference workloads at scale and low cost.
Quick Answer
The correct answer is asynchronous bulk processing of large inference request volumes at reduced cost. Azure OpenAI’s Batch API is designed for workloads where you need to handle high-throughput tasks—like generating chat completions or embeddings for thousands of documents—but don’t require immediate responses. Instead of making real-time API calls, you submit a batch of requests and retrieve the results later, which lowers cost by leveraging off-peak processing and batching efficiencies. On the AI-900 exam, this tests your understanding of when to choose batch over real-time inference; a common trap is assuming batch is faster, when in fact it trades speed for cost savings. The key distinction is latency sensitivity: if your application can wait minutes or hours for results, batch is ideal. Memory tip: think “Batch = Bulk + Backlog” — you pile up work and get it done cheaper, not quicker.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. 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.
What is 'Azure OpenAI's batch API' and when should you use it?
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
Asynchronous bulk processing of large inference request volumes at reduced cost
Azure OpenAI's Batch API is designed for asynchronous processing of large volumes of inference requests, such as chat completions or embeddings, at a reduced cost compared to real-time API calls. It is ideal for workloads where immediate responses are not required, allowing you to submit a batch of requests and retrieve results later. This makes it a cost-effective solution for high-throughput, non-latency-sensitive tasks.
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.
- ✗
An API for training new models in batches on your custom datasets
Why it's wrong here
Model training is fine-tuning — the batch API processes large volumes of inference requests asynchronously at lower cost.
- ✓
Asynchronous bulk processing of large inference request volumes at reduced cost
Why this is correct
Batch API runs high-volume jobs (thousands of requests) asynchronously within 24h at ~50% cost reduction — ideal for offline processing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Grouping multiple Azure OpenAI API keys into a batch for easier management
Why it's wrong here
API key management is Azure resource administration — the batch API is for bulk inference processing.
- ✗
A tool for running multiple prompt experiments simultaneously to find the best prompt
Why it's wrong here
Prompt experimentation is evaluation/testing — the batch API processes production inference workloads at scale and low cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse batch processing for inference with batch training of models, leading them to select Option A, but Azure OpenAI's Batch API is strictly for inference, not model training.
Detailed technical explanation
How to think about this question
Under the hood, the Batch API accepts a JSONL file containing multiple requests, each with a unique ID and parameters, and processes them asynchronously with a guaranteed throughput, often at a 50% cost reduction compared to standard pay-as-you-go pricing. The results are stored in an Azure Storage blob for retrieval, and the API supports retries and error handling per request. A real-world scenario is processing thousands of customer support ticket summaries overnight, where latency of a few hours is acceptable but cost savings are critical.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Asynchronous bulk processing of large inference request volumes at reduced cost — Azure OpenAI's Batch API is designed for asynchronous processing of large volumes of inference requests, such as chat completions or embeddings, at a reduced cost compared to real-time API calls. It is ideal for workloads where immediate responses are not required, allowing you to submit a batch of requests and retrieve results later. This makes it a cost-effective solution for high-throughput, non-latency-sensitive tasks.
What should I do if I get this AI-900 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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Last reviewed: Jun 11, 2026
This AI-900 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-900 exam.
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