Databricks-DA-Assoc Managing Data Practice Question
A data analyst has a Delta table silver.customers in Unity Catalog. A new privacy requirement states that analysts must never see the raw email column, but they still need to query all other columns. The analyst has CREATE VIEW and SELECT privileges on the schema. Which approach best enforces the requirement while keeping the table queryable?
⚠ Common exam trap
The trap here is thinking that a column mask or row filter removes a column from visibility, when those features alter values or rows rather than hiding the column entirely.
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
✓
Create a view that selects all columns except email, grant SELECT on the view to the analysts, and revoke SELECT on the base table.
Projecting only the safe columns into a view and revoking direct SELECT on the base table is the canonical least-privilege pattern in Unity Catalog. Analysts query the view, which returns every column except email, while the raw table remains inaccessible. Column masks or row filters do not remove the column from query results in the same unambiguous way.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a row filter to the table so rows containing emails are excluded for analysts, and keep base-table SELECT granted.
Why it's wrong here
Row-level filters remove rows, not columns. Analysts would still see the email column for rows that pass the filter, which violates the requirement that the raw email must never be visible. Row filters address horizontal visibility, whereas this scenario needs vertical projection control, making the approach a poor fit.
- ✓
Create a view that selects all columns except email, grant SELECT on the view to the analysts, and revoke SELECT on the base table.
Why this is correct
A view that projects only the non-sensitive columns, combined with revoking direct table access, gives analysts the data they need while preventing them from reading the email column. Because Unity Catalog views can run with the definer's privileges, analysts do not need base-table SELECT, so this cleanly enforces the privacy requirement.
- ✗
Rename the email column to a non-obvious name and rely on security by obscurity to hide it from analysts.
Why it's wrong here
Renaming a column does not restrict access; any user with SELECT on the table can still read the renamed column and discover its contents. Security by obscurity is not an access control mechanism and would fail an audit. The requirement demands that raw email values never be visible, which renaming cannot guarantee.
- ✗
Apply a column mask on the email column using a masking function, and leave SELECT on the base table in place.
Why it's wrong here
Column masks obscure values but do not prevent analysts from seeing the column's existence or from attempting to query it; they also require careful function design and privilege management. The requirement is that analysts must never see the raw email, and a mask can still return a value depending on the masking expression. A projection view with revoked table access is stricter and simpler here.
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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-DA-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-DA-Assoc exam.