Databricks-GenAI-Assoc Governance Practice Question
A GenAI engineer is developing a RAG application that uses a Vector Search index. The index is created from a Delta table that contains sensitive information. The security team requires that the engineer can audit which users have queried the index and what data they retrieved. Which Unity Catalog feature should the engineer enable to capture this information?
⚠ Common exam trap
The trap here is assuming that inference tables or lineage provide user-level query auditing, but only audit logs capture that information.
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
✓
Audit logs in system tables
Audit logs in system tables capture all access and query events, including those against Vector Search indexes. They record the user, action, and timestamp, enabling the engineer to audit who queried the index and when. This is the appropriate Unity Catalog feature for meeting the security team's auditing requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unity Catalog lineage
Why it's wrong here
Lineage tracks the flow of data and dependencies between objects, such as which tables were used to create an index. It does not record user queries or data retrieval events. While lineage is valuable for understanding data provenance, it does not provide the audit trail of user access required by the security team.
- ✗
Vector Search index metadata
Why it's wrong here
Vector Search index metadata contains information about the index configuration, such as the embedding model and source table, but it does not log user queries or retrieved documents. It is not an auditing mechanism and cannot provide the required information about who queried the index and what data they retrieved.
- ✗
Inference tables on the Vector Search endpoint
Why it's wrong here
Inference tables are used with model serving endpoints to log request and response payloads for monitoring. They are not available for Vector Search endpoints and do not capture query details or user identities for Vector Search. Therefore, they cannot fulfill the auditing requirement for index queries.
- ✓
Audit logs in system tables
Why this is correct
Audit logs in system tables capture access and query events across Databricks, including Vector Search index queries. They record the user identity, the action performed, and the timestamp. By querying the audit logs, the engineer can audit who queried the index and when, satisfying the security requirement. This is the standard mechanism for auditing access to Unity Catalog objects.
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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.