The correct approach is to use authorized views with aggregation functions. This works because an authorized view in BigQuery acts as a SQL wrapper that can perform aggregations like SUM, COUNT, or AVG on sensitive columns, then expose only the summarized results to analysts while keeping the underlying raw data hidden. By granting access to the view rather than the base table, you enforce column-level security without exposing individual records. On the Google Professional Data Engineer exam, this scenario tests your understanding of BigQuery’s native authorization model and the distinction between row-level and column-level controls—a common trap is confusing authorized views with IAM roles on tables, which would allow direct access. Remember: views are your gatekeepers, not your tables. A useful memory tip is “aggregate to segregate”—if you need to hide raw values, wrap them in an aggregation inside an authorized view.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company needs to process sensitive data in BigQuery with column-level security. They want to allow analysts to see aggregated data but not individual records. What approach?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use authorized views with aggregation functions
Option C is correct because authorized views in BigQuery allow you to define SQL queries that aggregate data (e.g., using SUM, COUNT, AVG) and expose only the aggregated results to analysts, while hiding individual records. This approach enforces column-level security by granting access to the view rather than the underlying table, ensuring analysts cannot query the raw data directly. It meets the requirement of seeing aggregated data without seeing individual records, leveraging BigQuery's native authorization and SQL capabilities.
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.
✗
Use table-level access controls
Why it's wrong here
Table-level controls give full access to the table, not aggregated.
✗
Use column-level access controls with masking
Why it's wrong here
Column-level access masks values but does not provide aggregation only.
✓
Use authorized views with aggregation functions
Why this is correct
Authorized views can present aggregated data while hiding raw details.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Cloud Data Loss Prevention to de-identify data
Why it's wrong here
DLP is for scanning and de-identification, not for fine-grained access control.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between column-level masking (which still allows row-level access) and authorized views (which enforce aggregation at the query level), leading candidates to pick B because they confuse masking with aggregation-based security.
Detailed technical explanation
How to think about this question
Authorized views in BigQuery work by creating a view that is granted access to the underlying table via the view's owner (a service account or user), while the analysts are granted access only to the view itself. The view's SQL can include aggregation functions like COUNT(*) or SUM(salary) GROUP BY department, ensuring that analysts never see raw rows. A subtle behavior is that if the view is not properly scoped (e.g., using SELECT * without aggregation), it could expose individual records, so the view definition must explicitly aggregate. In real-world scenarios, this is often combined with row-level security (e.g., using WHERE clauses) to further restrict data by geography or team.
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.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use authorized views with aggregation functions — Option C is correct because authorized views in BigQuery allow you to define SQL queries that aggregate data (e.g., using SUM, COUNT, AVG) and expose only the aggregated results to analysts, while hiding individual records. This approach enforces column-level security by granting access to the view rather than the underlying table, ensuring analysts cannot query the raw data directly. It meets the requirement of seeing aggregated data without seeing individual records, leveraging BigQuery's native authorization and SQL capabilities.
What should I do if I get this PDE 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This PDE 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 PDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.