Databricks-GenAI-Assoc Governance Practice Question
A GenAI engineer is deploying a RAG application that uses Databricks Vector Search and a Foundation Model API. The solution must comply with governance policies that require all data access and model invocations to be auditable and access-controlled at a fine-grained level. Which two Unity Catalog features should the engineer leverage to meet these requirements? (Choose two.)
⚠ Common exam trap
The trap here is assuming that storing model weights in Volumes or using cluster policies provides governance, but these do not enforce access control or audit model invocations.
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
✓
Unity Catalog privileges on the Vector Search index and the source Delta table.
Unity Catalog privileges on the Vector Search index and source table provide fine-grained access control, ensuring only authorized users can retrieve data. Inference tables for the Foundation Model API automatically log requests and responses, including user identity, creating an audit trail. Together, these features meet the access-control and auditability requirements for the RAG application.
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 volumes to store the model weights and configuration files.
Why it's wrong here
Volumes are used for storing and accessing files, but they do not provide fine-grained access control or auditing for model invocations or vector search queries. While you can store model artifacts in volumes, they do not log inference requests or enforce privileges on index queries. Thus, volumes alone do not meet the governance requirements.
- ✗
Cluster policies to restrict the types of clusters that can access the data.
Why it's wrong here
Cluster policies control cluster configurations and who can create clusters, but they do not govern data access or model invocations. They cannot enforce fine-grained privileges on Unity Catalog objects or log inference requests. Thus, cluster policies are not suitable for meeting the stated governance requirements.
- ✓
Unity Catalog privileges on the Vector Search index and the source Delta table.
Why this is correct
Unity Catalog privileges on the Vector Search index and the source Delta table allow fine-grained access control. You can grant SELECT on the index to specific groups and restrict access to the underlying table. This ensures that only authorized users can retrieve data and perform searches, meeting the access-control requirement for the RAG application.
- ✓
Inference tables for the Foundation Model API endpoint to log requests and responses.
Why this is correct
Inference tables capture each request and response from the Foundation Model API, including user identity and timestamp. This provides an audit trail for model invocations. By enabling inference tables, the engineer ensures that all model interactions are logged and can be reviewed for compliance, satisfying the auditability requirement.
- ✗
Databricks SQL dashboards to visualize access patterns and model usage.
Why it's wrong here
Dashboards are visualization tools and do not enforce access control or provide raw audit logs. They can display aggregated metrics, but they cannot capture per-request details or restrict who can query the index or invoke the model. Therefore, dashboards are not a governance mechanism for fine-grained access and auditing.
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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.