Databricks-DE-Pro Data Governance Practice Question
A data engineer has a Unity Catalog managed table `sales.raw.transactions` that contains a column `customer_email` with PII. Analysts in the `marketing_analysts` group need to query the table for aggregate reporting but must never see individual email addresses. The engineer wants to enforce this dynamically without creating a separate view or copy of the data. Which Unity Catalog feature should the engineer use?
⚠ Common exam trap
The trap here is assuming that row filters can restrict columns or that revoking table access and using a view is the only way to hide sensitive columns, overlooking the dynamic column mask feature.
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 to `customer_email` using a user-defined function that returns NULL for members of `marketing_analysts`.
A column mask in Unity Catalog applies a user-defined function at query time to transform or redact column values based on the invoking user's identity or group memberships. This allows the same table to return different values for different users without duplicating data or creating separate views. It is the native mechanism for dynamic column-level security and satisfies the requirement to hide email addresses from marketing analysts while still permitting aggregate queries.
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 a row filter on `sales.raw.transactions` that excludes rows where `customer_email` is not null.
Why it's wrong here
Row filters restrict which rows are visible, not which columns. Excluding rows where `customer_email` is not null would remove all rows with email data, which is not the goal. The requirement is to hide the email values while still allowing aggregates over other columns; a row filter would eliminate data rather than mask a column.
- ✗
Enable attribute-based access control (ABAC) at the catalog level to automatically mask all PII columns for all non-admin users.
Why it's wrong here
Unity Catalog does not provide a catalog-level ABAC feature that automatically identifies and masks all PII columns. While ABAC policies can be defined, they require explicit tagging and policy definitions. The scenario requires targeting a specific column for a specific group, which is directly achieved with a column mask, not a catalog-wide automatic mechanism.
- ✓
Apply a column mask to `customer_email` using a user-defined function that returns NULL for members of `marketing_analysts`.
Why this is correct
Column masks in Unity Catalog allow dynamic redaction of column values based on the querying user or group. By attaching a mask function that returns NULL for marketing_analysts, the engineer enforces the restriction at query time without altering the underlying data or creating separate views. This meets the requirement of dynamic enforcement and is applied directly to the table column.
- ✗
Revoke SELECT on the table from `marketing_analysts` and grant SELECT only on a view that omits `customer_email`.
Why it's wrong here
While creating a view that omits the column is a valid approach, it requires creating and maintaining a separate view and does not dynamically enforce the mask based on the querying group. The scenario explicitly asks for dynamic enforcement without creating a separate view or copy. Revoking and re-granting also changes access patterns unnecessarily.
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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.