AI-900 Practice Question: Describe features of generative AI workloads on Azure
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?
⚠ Common exam trap
A common mix-up: candidates 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.
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
✓
Azure OpenAI on Your Data (data grounding)
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.
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
Azure OpenAI On Your Data uses a retrieval augmented generation (RAG) pipeline: your legal documents are chunked, embedded, and indexed in Azure AI Search. At runtime, the service retrieves the most relevant passages based on the user query, inserts them into the prompt, and configures the model (via the 'strictness' setting) to answer only from those retrieved spans. This creates a hard grounding boundary that prevents reliance on the model's internal parametric knowledge, making it the only choice that can constrain the summary strictly to the firm's own documents.
- ✗
Content filtering
Why it's wrong here
Azure OpenAI content filtering is a safety layer that uses trained classifiers to detect and block categories like hate, sexual, violence, and self-harm in both prompts and completions. It does not inspect the factual provenance of the model's output, nor does it limit the model's ability to draw on general legal knowledge or invented details. While it may refuse to generate certain statements, it cannot enforce that the summary is derived solely from a specific uploaded document.
- ✗
Prompt engineering with system messages
Why it's wrong here
Prompt engineering through system messages allows you to define the assistant role, tone, response format, and explicit constraints, such as 'only use the provided text.' However, the model treats these instructions as soft constraints; because no retrieval mechanism injects the actual document content, the underlying transformer can still complete patterns based on its pretrained parameters. This can yield plausible but unsupported legal arguments or citations, so system messages are insufficient for controlled summarization.
- ✗
Fine-tuning the model on legal texts
Why it's wrong here
Fine-tuning on legal texts modifies the model's weights using a curated dataset, producing outputs that match legal vocabulary and drafting style. This supervised adaptation embeds domain patterns but does not create a per-document access boundary: after training, the model has no direct reference to any particular contract or filing, and it may confidently generate statements that are factually inconsistent with a given source. Additionally, fine-tuning requires substantial labeled data and still doesn't support traceable citations.
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Azure Machine Learning Studio
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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