20+ practice questions focused on Governance — one of the most tested topics on the Databricks Certified Generative AI Engineer Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Governance PracticeWhich TWO of the following statements correctly describe the capabilities of a Databricks metastore in Unity Catalog?
Explanation: The Unity Catalog metastore serves as the top-level container for data governance, centralizing metadata and access control across all workspaces in an account. It enables unified data discovery and governance by providing a single source of truth for tables, schemas, and volumes. Understanding this hierarchy is crucial for cross-workspace data sharing and maintaining consistent security policies throughout the entire Databricks environment.
A company requires that all data access in Databricks be restricted to a specific region for regulatory compliance. Which Unity Catalog feature should they use to enforce this?
Explanation: Unity Catalog's location-based governance allows organizations to restrict the storage locations used for data. By using external locations constrained to specific cloud regions, administrators ensure that all data assets registered in the catalog comply with regional residency requirements. This prevents data sprawl into unauthorized regions and simplifies the compliance audit process for global organizations.
A GenAI engineer is building a Retrieval-Augmented Generation (RAG) application with Databricks Vector Search. The index is created in Unity Catalog and points to a Delta table containing confidential customer support transcripts. The engineer wants to ensure that only members of the 'genai_team' group can query the index, while other users in the workspace cannot see it. What is the correct way to enforce this access control?
Explanation: Vector Search indexes in Unity Catalog are independent securable objects. To allow only the 'genai_team' group to query the index, an administrator must grant the necessary privileges on the index itself: SELECT on the index, plus USE CATALOG and USE SCHEMA on its parent catalog and schema. This ensures that other users cannot access the index, meeting the confidentiality requirement.
A GenAI engineer is using Databricks Foundation Model APIs to serve a Llama 3 model. The engineer needs to log all inference requests and responses for auditing purposes, including the user identity and the model version. The logs must be stored in a Unity Catalog table with column-level encryption for the request text. Which combination of features should the engineer use?
Explanation: Inference tables are a built-in feature of Databricks Model Serving that automatically log request and response payloads, along with metadata such as the user, timestamp, and model version. To protect sensitive request text, a column mask can be applied to the request column in the inference table. This combination fulfills the auditing and encryption requirements without custom code.
A GenAI engineer has built a Retrieval-Augmented Generation (RAG) application in a Databricks notebook. The application uses the `databricks-bge-large-en` Foundation Model for embedding queries and retrieves documents from a Delta table. The security team requires that all calls to the embedding model be logged with the user identity for audit purposes, without changing any application code. Which Unity Catalog feature should the engineer implement?
Explanation: Unity Catalog model serving with on-behalf-of authentication passes the identity of the calling user to the serving endpoint, ensuring that model invocations, including Foundation Model API calls, are logged with the correct user in audit logs. This satisfies the requirement without altering application code, as the authentication is handled at the platform level. Other options either require code changes or do not capture identity for audit.
+15 more Governance questions available
Practice all Governance questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Governance. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Governance questions on the Databricks-GenAI-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Governance is tested as part of the Databricks Certified Generative AI Engineer Associate blueprint. Practicing with targeted Governance questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free Databricks-GenAI-Assoc practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Governance is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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