Databricks-DE-Pro Data Security and Compliance Practice Question
A Data Engineer needs to ensure that PII data in a Delta table is accessible only to members of the 'hr_admin' group, while allowing all other users to view the non-PII columns. Which Unity Catalog feature is the most efficient way to implement this requirement?
⚠ Common exam trap
Candidates frequently suggest creating duplicate filtered views or tables for different user groups, ignoring Unity Catalog's modern column masking capabilities which provide centralized efficiency.
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
✓
Apply a column mask using a SQL function in Unity Catalog.
Unity Catalog's row-level security and column-level masking allow for fine-grained access control directly at the table level. By defining a masking function or a column filter, the Data Engineer ensures that the security policy is enforced consistently across all SQL warehouses and Databricks Runtime versions. This approach centralizes governance, reduces administrative overhead compared to view-based security, and ensures that data privacy compliance is maintained without duplicating data or creating multiple table versions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create separate physical tables for HR and general users.
Why it's wrong here
Creating separate physical tables leads to data duplication, increased storage costs, and significant data synchronization challenges. Maintaining consistency across multiple tables is prone to human error, making it difficult to audit and ensure that both tables reflect the same source of truth for compliance reporting.
- ✗
Use standard SQL views for every user to filter columns.
Why it's wrong here
While views can restrict column access, they are difficult to manage at scale as the number of users and roles grows. Furthermore, views do not provide the same centralized metadata lineage and access auditing capabilities that Unity Catalog's native column masking and filtering features provide.
- ✓
Apply a column mask using a SQL function in Unity Catalog.
Why this is correct
Column masking in Unity Catalog allows administrators to define functions that dynamically redact or obscure data based on the user's role. This provides a unified, policy-driven approach to data security that is applied at query time, ensuring compliance without the complexity of managing numerous views or physical tables.
- ✗
Assign the 'SELECT' permission on individual columns in the UI.
Why it's wrong here
Databricks Unity Catalog permissions are primarily managed at the table, schema, or catalog level. While column-level security is supported via masking and filtering, there is no UI checkbox feature to simply grant or revoke SELECT access on individual columns without defining a masking policy or filter.
About these practice questions
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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-DE-Pro 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-DE-Pro exam.