Databricks-GenAI-Assoc Governance Practice Question
A company is preparing a generative AI application for production and must demonstrate that model inputs and outputs are traceable and that access to sensitive prompt data is controlled. Which TWO Unity Catalog capabilities should the team rely on to meet these governance objectives? (Choose two.)
⚠ Common exam trap
The trap here is selecting performance features like caching or tuning as governance controls, when governance needs enforcement plus audit evidence.
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
✓
Fine-grained privileges on catalogs, schemas, tables, and models
Meeting the governance objectives requires both an enforcement mechanism and an evidence trail. Fine-grained privileges on the relevant catalogs, schemas, tables, and models enforce least-privilege access to sensitive prompt data, while Unity Catalog audit logs record which principals accessed those governed objects, providing the traceability that production compliance reviews demand.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-grained privileges on catalogs, schemas, tables, and models
Why this is correct
Unity Catalog's privilege model lets administrators grant narrowly scoped rights such as SELECT on a table or EXECUTE on a model to specific groups. This enforces least privilege on sensitive prompt data and model invocation, which is the second governance objective in the scenario and complements the audit trail.
- ✗
Embedding caching to reduce inference latency
Why it's wrong here
Caching embeddings is a performance optimization that lowers latency and cost. It neither restricts who can read prompt data nor records access events, so it cannot satisfy the traceability and access-control objectives. In fact, caching outside governed storage could create an ungoverned copy of sensitive content.
- ✗
Vector index partitioning by document topic
Why it's wrong here
Partitioning a vector index by topic is an organizational and retrieval-efficiency choice. It does not enforce authorization or produce audit evidence, so while it may help relevance, it does not meet the stated governance goals of traceable access and controlled sensitive prompt data.
- ✓
Audit logs that record which principals accessed governed tables and models
Why this is correct
Unity Catalog emits audit events for data and model access, capturing the principal, the object, and the action. These logs give the team the traceability evidence needed to show who touched sensitive prompt data and when, which directly supports the governance objective of demonstrating controlled, attributable access in production.
- ✗
Automatic hyperparameter tuning of registered models
Why it's wrong here
Hyperparameter tuning improves model quality metrics; it does not create access controls or audit records for prompt data. Nothing about tuning demonstrates who accessed what or restricts sensitive inputs, so it does not advance either governance objective described in the scenario and is unrelated to the compliance 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.