Databricks-GenAI-Assoc Application Development Practice Question
A GenAI engineer is building a retrieval-augmented chatbot whose answers must cite the exact source document and page. The team wants the chatbot's responses to include structured citations that downstream UIs can render. Which Databricks feature should the engineer use to return these structured citations from the model endpoint?
⚠ Common exam trap
The trap here is equating observability artifacts such as MLflow traces or inference tables with the response payload, when only the agent's response schema can deliver citations to the UI.
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
✓
Mosaic AI Agent Framework's document citation support, where the retriever returns chunks with metadata and the agent formats them into a structured citations field in the response.
Structured citations must be part of the model endpoint's response contract, not a side channel. The Mosaic AI Agent Framework's citation support lets the retriever supply chunk metadata and the agent emit a citations field the UI can render directly. Observability traces, retrieval reranking, and payload logging serve different purposes and cannot substitute for returning structured citations to the caller at inference time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Vector Search's built-in reranking, which automatically appends source metadata to the model's generated answer.
Why it's wrong here
Vector Search returns retrieved chunks with their metadata, but it does not generate answers and does not append citations to an LLM's output. Reranking only reorders candidates by relevance. Attributing citations to a generated response requires the application or model to construct them, so this option misattributes a generation-time responsibility to the retrieval service.
- ✗
MLflow Tracing with span attributes that capture the retrieved document IDs and page numbers for each LLM call.
Why it's wrong here
MLflow Tracing records execution details for observability and debugging, but it does not deliver structured citations back to the calling application as part of the response payload. Traces are inspected in the MLflow UI or via the tracing API, not rendered by a chatbot UI at inference time. This option confuses observability telemetry with the response contract the scenario requires.
- ✓
Mosaic AI Agent Framework's document citation support, where the retriever returns chunks with metadata and the agent formats them into a structured citations field in the response.
Why this is correct
The Mosaic AI Agent Framework supports returning retrieved documents alongside the model's answer, exposing a structured citations payload that includes source identifiers and metadata such as page numbers. This is precisely the mechanism for delivering renderable citations from a deployed agent endpoint. It pairs the retriever's chunk metadata with the agent's response schema, matching the scenario's requirement.
- ✗
Mosaic AI Model Serving's payload logging, which writes each request and response to an inference table that the UI can read for citations.
Why it's wrong here
Payload logging to inference tables is designed for monitoring, debugging, and downstream analytics, not for returning citations in the live response. The UI would have to query the inference table separately and correlate requests, which is fragile and unsuitable for real-time rendering. This option mistakes a logging feature for a response-shaping feature, so it does not meet the requirement.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.