The correct answer is Azure OpenAI on Your Data (data grounding). This feature uses retrieval-augmented generation (RAG) to restrict the model’s responses strictly to the provided contract text, ensuring no external knowledge or hallucinated facts are introduced. By indexing the firm’s contract documents, the model retrieves and generates summaries based solely on that source data, effectively eliminating hallucinations. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control model outputs for accuracy and compliance, often appearing in scenarios requiring fact-based responses from proprietary data. A common trap is confusing prompt engineering or content filtering with data grounding—remember that only data grounding ties the model to a specific dataset. Memory tip: think “grounded in your data” to recall that the model cannot stray beyond the documents you provide.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
A legal firm wants to use Azure OpenAI to generate summaries of lengthy contracts. The firm requires that the generated summaries are strictly based on the provided contract text and do not include any external knowledge or hallucinated facts. Which Azure OpenAI feature should the firm configure to meet this requirement?
This feature connects the model to your documents, ensuring answers are based only on the provided content.
B
Content filtering
Why wrong: Content filtering blocks offensive or unsafe content but does not restrict the model to using only the provided document.
C
Prompt engineering with system messages
Why wrong: System messages can guide the model's behavior but cannot guarantee that the model will not use external knowledge.
D
Fine-tuning the model on legal texts
Why wrong: Fine-tuning adapts the model to a domain but does not restrict its output to a specific document; it may still generate external information.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure OpenAI on Your Data (data grounding)
Option A is correct because Azure OpenAI on Your Data (data grounding) restricts the model's responses to the content of the provided contract documents, preventing the generation of information not present in the source text. This feature uses a retrieval-augmented generation (RAG) approach, where the model only references the indexed contract data, effectively eliminating external knowledge or hallucinated facts.
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.
✓
Azure OpenAI on Your Data (data grounding)
Why this is correct
This feature connects the model to your documents, ensuring answers are based only on the provided content.
Related concept
Read the scenario before looking for a memorised answer.
✗
Content filtering
Why it's wrong here
Content filtering blocks offensive or unsafe content but does not restrict the model to using only the provided document.
✗
Prompt engineering with system messages
Why it's wrong here
System messages can guide the model's behavior but cannot guarantee that the model will not use external knowledge.
✗
Fine-tuning the model on legal texts
Why it's wrong here
Fine-tuning adapts the model to a domain but does not restrict its output to a specific document; it may still generate external information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse content filtering (which blocks unsafe output) with data grounding (which restricts output to a specific dataset), or they assume fine-tuning alone can prevent hallucination, when in reality fine-tuning does not eliminate the model's tendency to generate information beyond the given input.
Trap categories for this question
Command / output trap
Fine-tuning adapts the model to a domain but does not restrict its output to a specific document; it may still generate external information.
Detailed technical explanation
How to think about this question
Under the hood, Azure OpenAI on Your Data uses a RAG pipeline where the contract text is chunked, embedded, and indexed in Azure Cognitive Search. At inference time, the user's query is used to retrieve relevant chunks, which are then injected into the prompt as context, forcing the model to generate responses grounded in those chunks. A subtle behavior is that if the retrieved chunks are incomplete or irrelevant, the model may still produce summaries that miss key details, but it will not invent facts outside the provided data.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Azure OpenAI on Your Data (data grounding) — Option A is correct because Azure OpenAI on Your Data (data grounding) restricts the model's responses to the content of the provided contract documents, preventing the generation of information not present in the source text. This feature uses a retrieval-augmented generation (RAG) approach, where the model only references the indexed contract data, effectively eliminating external knowledge or hallucinated facts.
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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