- A
Training a custom Azure OpenAI model exclusively on your proprietary data
Why wrong: Model training on proprietary data is fine-tuning — 'on your data' is a managed RAG feature, not model training.
- B
A managed RAG feature that answers questions from your connected data sources without custom pipeline code
'On your data' connects Azure OpenAI to your documents — automatically handling retrieval and grounding for enterprise Q&A.
- C
Restricting Azure OpenAI to only use data from your Azure subscription, blocking external knowledge
Why wrong: Data isolation is a security concern — 'on your data' is an additive RAG feature that supplements the model with your documents.
- D
A billing option that charges based on the volume of your data processed rather than tokens
Why wrong: Azure OpenAI charges per token — 'on your data' is a functionality feature for document Q&A, not a billing model.
Azure OpenAI On Your Data: What It Is and How It Enables RAG
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 on your data' and what does it enable?
Quick Answer
The correct answer is that Azure OpenAI on your data is a managed Retrieval Augmented Generation (RAG) feature. This is the right choice because it allows you to connect Azure OpenAI models directly to your own data sources—such as Azure Blob Storage, Cosmos DB, or Azure AI Search—without needing to write any custom orchestration code. By grounding responses in your proprietary data, it improves accuracy and relevance while significantly reducing hallucinations. On the AI-900 exam, this concept tests your understanding of how to integrate AI models with enterprise data for secure, context-aware answers; a common trap is confusing it with a simple search or fine-tuning solution. Remember, it is not about retraining the model—it is about augmenting the prompt with your data in real time. Memory tip: think of it as “bring your own data, no code required.”
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
A managed RAG feature that answers questions from your connected data sources without custom pipeline code
Option B is correct because 'Azure OpenAI on your data' is a managed Retrieval Augmented Generation (RAG) feature that allows you to connect Azure OpenAI models directly to your data sources (e.g., Azure Blob Storage, Azure Cosmos DB, or Azure AI Search) without writing custom orchestration code. It enables the model to ground its responses in your proprietary data, improving accuracy and relevance while reducing hallucinations.
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.
- ✗
Training a custom Azure OpenAI model exclusively on your proprietary data
Why it's wrong here
Model training on proprietary data is fine-tuning — 'on your data' is a managed RAG feature, not model training.
- ✓
A managed RAG feature that answers questions from your connected data sources without custom pipeline code
Why this is correct
'On your data' connects Azure OpenAI to your documents — automatically handling retrieval and grounding for enterprise Q&A.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Restricting Azure OpenAI to only use data from your Azure subscription, blocking external knowledge
Why it's wrong here
Data isolation is a security concern — 'on your data' is an additive RAG feature that supplements the model with your documents.
- ✗
A billing option that charges based on the volume of your data processed rather than tokens
Why it's wrong here
Azure OpenAI charges per token — 'on your data' is a functionality feature for document Q&A, not a billing model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'using your data for grounding' with 'training a custom model on your data,' leading them to incorrectly select Option A, even though Azure OpenAI on your data does not involve any model training or fine-tuning.
Detailed technical explanation
How to think about this question
Under the hood, 'Azure OpenAI on your data' uses a RAG architecture where user queries are first sent to Azure AI Search to retrieve relevant chunks from your indexed data, then the query and retrieved context are combined into a prompt sent to the Azure OpenAI model. This eliminates the need for custom vectorization or orchestration code, as the service handles chunking, embedding, and retrieval automatically. A real-world scenario is a customer support chatbot that answers questions from a knowledge base stored in Azure Blob Storage, ensuring responses are always based on the latest documentation.
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.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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: A managed RAG feature that answers questions from your connected data sources without custom pipeline code — Option B is correct because 'Azure OpenAI on your data' is a managed Retrieval Augmented Generation (RAG) feature that allows you to connect Azure OpenAI models directly to your data sources (e.g., Azure Blob Storage, Azure Cosmos DB, or Azure AI Search) without writing custom orchestration code. It enables the model to ground its responses in your proprietary data, improving accuracy and relevance while reducing hallucinations.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
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