Databricks Data Engineer Professional Question 199
Single answerA data engineering team is tasked with implementing fine-grained access control for a Delta table in Databricks to ensure that users can only query specific columns based on their roles. Which feature in Databricks should they use to achieve this?
- A
Cluster policies
- B
Table ACLs (Access Control Lists)
- C
Unity Catalog column-level permissions
- D
Data masking
Show answer and explanation
Correct answer: C
Explanation
Implementing fine-grained access control at the column level in Databricks requires using Unity Catalog column-level permissions. This allows you to define policies that restrict access to specific columns based on user roles, ensuring compliance with security and governance requirements. Other options, such as Table ACLs or data masking, either lack the required granularity or serve different purposes.
- A. Incorrect.
Cluster policies are used to enforce restrictions on cluster configurations, such as limiting instance types or scaling options, but they do not provide fine-grained access control for tables or columns.
- B. Incorrect.
Table ACLs allow role-based access control at the table level, but they do not provide column-level granularity for access control.
- C. Correct.
Unity Catalog column-level permissions enable fine-grained access control, allowing you to restrict access to specific columns in a table based on user roles. This is the correct feature to apply in this scenario.
- D. Incorrect.
Data masking is used to obfuscate sensitive data in a table, but it is not a mechanism for controlling which users can query specific columns.