- A
IAM conditions on tables
Why wrong: IAM conditions cannot mask column values.
- B
BigQuery Data Policy with data masking rules
Data masking rules in BigQuery Data Policy allow column-level masking without changing the underlying data.
- C
Authorized views with row-level security
Why wrong: Row-level security restricts rows, not columns, and requires views.
- D
Cloud DLP de-identification transforms on export
Why wrong: DLP is for external scanning and transformation, not real-time query masking.
Question 457 of 985
PCSE Ensuring Data Protection Practice Question
This PCSE practice question tests your understanding of ensuring data protection. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A healthcare company stores patient data in BigQuery and needs to mask sensitive columns like SSN and email for analysts who do not need to see the actual values. They want to apply consistent masking across queries without modifying the underlying data. Which feature 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
✓
BigQuery Data Policy with data masking rules
BigQuery Data Policy with data masking rules allows you to define column-level masking policies that automatically redact sensitive data (e.g., SSN, email) in query results based on the user's role, without altering the underlying table. This meets the requirement for consistent masking across all queries without modifying the source data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
IAM conditions on tables
Why it's wrong here
IAM conditions cannot mask column values.
- ✓
BigQuery Data Policy with data masking rules
Why this is correct
Data masking rules in BigQuery Data Policy allow column-level masking without changing the underlying data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Authorized views with row-level security
Why it's wrong here
Row-level security restricts rows, not columns, and requires views.
- ✗
Cloud DLP de-identification transforms on export
Why it's wrong here
DLP is for external scanning and transformation, not real-time query masking.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between row-level security (which filters rows) and column-level masking (which obfuscates column values), leading candidates to mistakenly choose authorized views when the requirement is to mask sensitive columns, not restrict rows.
Detailed technical explanation
How to think about this question
BigQuery Data Policies use taxonomy-based policy tags attached to columns, and the masking rule (e.g., 'Default Masking' or 'Hash') is enforced at query runtime by the BigQuery engine, ensuring that even if an analyst runs a SELECT *, the sensitive columns are automatically redacted. A subtle behavior is that masking policies can be overridden for users with the 'roles/bigquery.dataViewer' role if they are granted explicit access via a separate policy, so careful role management is required to prevent unintended exposure.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Ensuring Data Protection — study guide chapter
Learn the concepts, then practise the questions
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Ensuring Data Protection practice questions
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FAQ
Questions learners often ask
What does this PCSE question test?
Ensuring Data Protection — This question tests Ensuring Data Protection — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery Data Policy with data masking rules — BigQuery Data Policy with data masking rules allows you to define column-level masking policies that automatically redact sensitive data (e.g., SSN, email) in query results based on the user's role, without altering the underlying table. This meets the requirement for consistent masking across all queries without modifying the source data.
What should I do if I get this PCSE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This PCSE practice question is part of Courseiva's free Google Cloud 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 PCSE exam.
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