Databricks-GenAI-Assoc Governance Practice Question
A data scientist is developing a GenAI application that uses a foundation model served via Databricks Model Serving. The model endpoint is configured to log inference tables for monitoring. The data science team wants to ensure that the inference logs, which may contain sensitive user prompts, are protected according to Unity Catalog policies. Which Unity Catalog object should be used to store and govern the inference tables?
⚠ Common exam trap
The trap here is thinking that inference logs are automatically governed or that a volume is appropriate; inference logs are tabular Delta tables and should be stored as Unity Catalog tables to leverage fine-grained access controls.
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
✓
A managed table in a Unity Catalog schema with appropriate grants and column masks.
Inference tables logged by Databricks Model Serving are Delta tables that can be stored in Unity Catalog. Using a managed table in a Unity Catalog schema allows the team to apply Unity Catalog governance features such as grants, column masks, and row filters to protect sensitive data. This ensures that the inference logs are subject to the same security policies as other data assets, providing centralized governance and auditability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A managed table in a Unity Catalog schema with appropriate grants and column masks.
Why this is correct
Inference tables logged by Databricks Model Serving are Delta tables that can be stored in Unity Catalog. By storing them as managed tables in a Unity Catalog schema, the team can apply Unity Catalog governance, including grants, row filters, and column masks, to protect sensitive prompt data. This integrates inference logging with the existing security model, ensuring that access to logs is controlled and auditable.
- ✗
A volume in Unity Catalog with file-level access controls.
Why it's wrong here
Volumes are used for unstructured data like files, not for tabular inference logs. Inference tables are structured Delta tables, so they should be stored as tables, not volumes. Volumes do not support fine-grained row and column security, which are essential for protecting sensitive prompt data. Using a volume would limit the ability to apply SQL-based governance policies.
- ✗
An external table pointing to a cloud storage location with a storage credential.
Why it's wrong here
While external tables can be governed by Unity Catalog, they require additional configuration and do not automatically inherit the same level of management as managed tables. For inference logs, which are generated by Databricks, a managed table is simpler and ensures that the data is stored in the Unity Catalog-managed storage location. External tables are better suited for data that already resides in external storage and needs to be registered.
- ✗
A view that dynamically redacts sensitive columns from the inference logs.
Why it's wrong here
A view can provide redaction, but it is a read-time construct and does not store the data. The inference logs must be stored somewhere; the question asks which object should be used to store and govern them. A view alone cannot serve as the storage object. The storage should be a table, and then views or column masks can be applied on top for redaction.
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.