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AI-102 Implement generative AI solutions Practice Question

You are building an internal knowledge assistant with Azure OpenAI Service. Responses must be grounded in a curated set of HR policy documents stored in Azure AI Search, and every response must include a citation to the specific source chunk. You already deployed a GPT-4o model and created the search index. You need to add the grounding layer with the least development effort. What should you do?

⚠ Common exam trap

The trap here is assuming that fine-tuning or larger token limits can substitute for retrieval when the requirement is verifiable citations from a private document set.

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

✓

Enable the "On Your Data" feature on the model deployment and point it at the Azure AI Search index.

Grounding a deployment in a private index with automatic citations is exactly what the On Your Data capability provides for Azure OpenAI. Since the Azure AI Search index is already built, enabling this feature on the deployment satisfies the grounding and citation requirements with configuration instead of custom code, which matches the least-effort constraint.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Increase the model temperature and max tokens so the model has more room to recall the HR policies from its pretrained knowledge.
Why it's wrong here
The base model was never trained on your private HR documents, so raising temperature and token limits only increases creative, ungrounded output and hallucination risk. Higher temperature specifically makes responses less deterministic, which is the opposite of what an auditable policy assistant needs. No citation mechanism is introduced by these parameter changes.
✗
Call the Azure AI Search REST API from the client, concatenate the top results into the system message, and post-process the model output to append source links.
Why it's wrong here
This works technically but requires custom retrieval logic, prompt assembly, and citation post-processing in your own code. The scenario explicitly asks for the least development effort, and the built-in On Your Data feature already performs these steps. Hand-rolling the pipeline adds maintenance burden and risk of citation mismatches without delivering additional value here.
✗
Fine-tune the GPT-4o deployment on the HR policy documents, then rely on the model to quote the correct policy section.
Why it's wrong here
Fine-tuning teaches style and patterns, not reliable retrieval of specific chunks, and it does not produce verifiable citations to source documents. It also requires a training dataset and retraining whenever policies change. The scenario needs grounded citations from a search index, which fine-tuning alone cannot guarantee, making it unsuitable for an auditable HR assistant.
✓
Enable the "On Your Data" feature on the model deployment and point it at the Azure AI Search index.
Why this is correct
On Your Data is a built-in Azure OpenAI capability that connects a model deployment directly to an Azure AI Search index, retrieving relevant chunks and returning citations automatically. Because the index already exists, this requires only configuration rather than custom retrieval code, satisfying the least-effort requirement while still grounding every answer in the curated HR documents.

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AI-102 Implement generative AI solutions Practice Question

Why each option matters

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