PCSE Ensuring Data Protection Practice Question
An organization stores sensitive customer data in BigQuery tables. They need to enforce column-level security such that users in the 'support' group see a masked version of email addresses (e.g., j***@example.com), while managers see the full email. Which approach should they use?
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 BigQuery data masking policy using policy tags on the email column.
BigQuery column-level security with policy tags and data masking rules can achieve this. Data masking policies can be applied to policy tags to mask data based on user access. Using views or separate tables is less maintainable, and Cloud DLP is for batch scanning/de-identification, not real-time masking.
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 view that applies masking functions and grant access to the view.
Why it's wrong here
Views can mask data but do not natively enforce role-based masking; every user sees the same view.
- ✗
Use Cloud DLP to scan and de-identify the data in place.
Why it's wrong here
DLP de-identification is a one-time transformation, not dynamic masking per user.
- ✓
Apply a BigQuery data masking policy using policy tags on the email column.
Why this is correct
Data masking policies allow dynamic masking based on the user's role when querying the table.
- ✗
Create separate tables for each role and populate them with appropriate data.
Why it's wrong here
This leads to data duplication and maintenance overhead.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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