- A
Use column-level security to hide sensitive columns
Why wrong: Column-level security hides columns but does not filter rows by region.
- B
Use BigQuery row-level access policies
Why wrong: BigQuery does not have built-in row-level access policies; this is not a supported feature.
- C
Create an authorized view that uses SESSION_USER() in a WHERE clause to filter rows
Authorized views can leverage the current user identity to dynamically filter rows, enabling row-level security.
- D
Create separate IAM roles for each region
Why wrong: IAM roles grant access to entire tables, not specific rows.
Question 868 of 1,000
Define data structures and implement SQL for Business Intelligence →hardMultiple ChoiceObjective-mapped
Implementing Row-Level Security with Authorized Views in BigQuery
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: authorized views. 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 BI manager needs to restrict access to sensitive sales data so that salespeople can only see their own region's data. Which BigQuery feature should be used to implement row-level security without duplicating tables?
Quick Answer
The correct answer is to create an authorized view that uses SESSION_USER() in a WHERE clause to filter rows. This is correct because the SESSION_USER() function dynamically returns the email address of the caller running the query, allowing the authorized view to apply row-level security by comparing that identity against a mapping table of users to regions—no table duplication is needed. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of how authorized views decouple data access from underlying tables, a common trap being to mistakenly choose table-level access controls or static filters that require separate copies of data. Remember that SESSION_USER() is evaluated at query runtime, not view creation time, making it ideal for multi-tenant environments. Memory tip: Think “SESSION_USER() = security per session, not per table.”
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 an authorized view that uses SESSION_USER() in a WHERE clause to filter rows
BigQuery supports row-level access policies as a native feature (Option B), but the requirement can also be met by creating an authorized view that uses SESSION_USER() in a WHERE clause (Option C). Option C is the correct answer because it provides a straightforward method to restrict data access without duplicating tables, leveraging the view's ability to filter rows dynamically based on the caller's identity. This approach is well-documented for implementing row-level security in BigQuery.
Key principle: Authorized views
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use column-level security to hide sensitive columns
Why it's wrong here
Column-level security hides columns but does not filter rows by region.
- ✗
Use BigQuery row-level access policies
Why it's wrong here
BigQuery does not have built-in row-level access policies; this is not a supported feature.
- ✓
Create an authorized view that uses SESSION_USER() in a WHERE clause to filter rows
Why this is correct
Authorized views can leverage the current user identity to dynamically filter rows, enabling row-level security.
Related concept
Authorized views
- ✗
Create separate IAM roles for each region
Why it's wrong here
IAM roles grant access to entire tables, not specific rows.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates may assume BigQuery does not have built-in row-level access policies and thus dismiss Option B. However, BigQuery does support row-level access policies, but the correct answer is Option C because the question specifically asks for a feature that restricts access without duplicating tables; authorized views with SESSION_USER() serve this purpose effectively.
Detailed technical explanation
How to think about this question
Under the hood, SESSION_USER() returns the email of the authenticated user running the query, which can be joined to a mapping table (e.g., user_region) to filter rows dynamically. This pattern avoids table duplication and leverages BigQuery's shared dataset model, where the view is authorized via a separate dataset's authorized views list, ensuring the underlying table remains private. A real-world scenario is a multinational company where sales managers in different countries query the same sales table but see only their country's data, with the mapping table maintained in a separate, secure dataset.
KKey Concepts to Remember
- Authorized views
- SESSION_USER()
- Row-level security
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
Authorized views
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.
Review authorized views, then practise related PCDE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PCDE question test?
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Authorized views.
What is the correct answer to this question?
The correct answer is: Create an authorized view that uses SESSION_USER() in a WHERE clause to filter rows — BigQuery supports row-level access policies as a native feature (Option B), but the requirement can also be met by creating an authorized view that uses SESSION_USER() in a WHERE clause (Option C). Option C is the correct answer because it provides a straightforward method to restrict data access without duplicating tables, leveraging the view's ability to filter rows dynamically based on the caller's identity. This approach is well-documented for implementing row-level security in BigQuery.
What should I do if I get this PCDE question wrong?
Review authorized views, then practise related PCDE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Authorized views
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Last reviewed: Jun 25, 2026
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