Databricks-GenAI-Assoc Design Applications Practice Question
An engineer is designing a RAG application that uses Databricks Vector Search to retrieve documents and a foundation model endpoint to generate answers. The team wants to log all user queries, retrieved documents, and generated responses for auditing and continuous improvement. They also need to monitor for drift in retrieval quality over time. Which Databricks capability should they integrate into the application design?
⚠ Common exam trap
It's easy for candidates to confuse governance auditing (Unity Catalog) or data pipeline quality (Delta Live Tables) with application-level tracing and payload logging, which are needed to monitor retrieval quality and improve the RAG application.
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
✓
MLflow Tracing with inference tables on the model serving endpoint.
MLflow Tracing provides detailed spans across the RAG pipeline, capturing queries, retrieved documents, and LLM responses. Inference tables on the serving endpoint automatically log request and response payloads for auditing. This combination enables both observability and drift monitoring. Other options focus on data pipelines, dashboards, or governance, which do not capture the runtime application behavior needed for continuous improvement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Live Tables with expectations to validate the retrieved documents.
Why it's wrong here
Delta Live Tables is for building reliable data pipelines with data quality expectations, not for logging user queries or tracing LLM calls. It does not capture runtime interactions between the application and the model endpoint. While useful for data processing, it does not provide the observability needed for auditing and drift monitoring of a RAG application. It is not the right tool for this scenario.
- ✗
Databricks SQL dashboards querying the source Delta table for document updates.
Why it's wrong here
SQL dashboards can visualize data but do not automatically log user queries or model responses. They also do not trace the retrieval and generation steps. Monitoring document updates is different from monitoring retrieval quality drift. This approach lacks the end-to-end logging and tracing required for auditing and continuous improvement of the RAG application.
- ✗
Unity Catalog audit logs and lineage for the Vector Search index.
Why it's wrong here
Unity Catalog audit logs capture access and governance events, not the content of user queries or model responses. Lineage shows data flow but not runtime interactions. While useful for security auditing, these features do not provide the detailed tracing and payload logging needed to monitor retrieval quality drift or improve the application based on user interactions.
- ✓
MLflow Tracing with inference tables on the model serving endpoint.
Why this is correct
MLflow Tracing captures detailed spans for each step of a generative AI application, including retrieval and LLM calls, enabling end-to-end observability. Inference tables on the serving endpoint automatically log request and response payloads for auditing. Together they provide the query, retrieved documents, and generated response logging, plus monitoring for drift. This is the recommended Databricks approach for tracing and logging generative AI applications.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.