- A
Create a user-defined function (UDF) that aggregates the data and grant analysts permission to call the UDF.
Why wrong: B is wrong because UDFs cannot restrict access to underlying tables; analysts would still need table access.
- B
Use BigQuery row-level security to restrict access to non-PII rows only.
Why wrong: D is wrong because row-level security filters rows, not columns, and cannot mask PII values within rows.
- C
Create an authorized view that does not include the PII columns and grant analysts access to the view.
Why wrong: A is wrong because authorized views are static; adding new columns requires view updates, and aggregate queries on non-PII columns are fine, but view selection is limited.
- D
Use BigQuery column-level security with data masking to mask the PII columns for the analysts' role.
C is correct because column-level masking dynamically masks data based on user permissions without changing the table structure.
Quick Answer
The answer is to use BigQuery column-level security with data masking to mask the PII columns for the analysts' role. This approach is correct because it allows you to define masking policies—such as a `DEFAULT_MASKING_RULE` that replaces sensitive values with hashes or nulls—directly on the PII columns, which automatically transforms the data for analysts while still permitting aggregate queries like `COUNT`, `SUM`, and `AVG` over the entire dataset. On the Google Professional Data Engineer exam, this scenario tests your understanding of fine-grained access control versus row-level security; a common trap is choosing row-level security, which filters rows instead of masking column values, thereby breaking aggregate accuracy. Remember the key distinction: column-level security masks values without removing rows, preserving the integrity of aggregations. Memory tip: “Mask the column, not the row—aggregates still flow.”
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 grant analysts access to a BigQuery table that contains sensitive PII columns. The analysts should be able to run aggregate queries on the entire dataset but must not see individual PII values. Which approach should the team 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
Use BigQuery column-level security with data masking to mask the PII columns for the analysts' role.
Option D is correct because BigQuery column-level security with data masking allows you to define masking policies on specific PII columns (e.g., using `DEFAULT_MASKING_RULE` or custom policies) that automatically transform the data for analysts' roles while still permitting aggregate queries over the entire dataset. This approach ensures analysts never see individual PII values, yet they can run `COUNT`, `SUM`, `AVG`, etc., on the masked columns, meeting both requirements precisely.
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.
- ✗
Create a user-defined function (UDF) that aggregates the data and grant analysts permission to call the UDF.
Why it's wrong here
B is wrong because UDFs cannot restrict access to underlying tables; analysts would still need table access.
- ✗
Use BigQuery row-level security to restrict access to non-PII rows only.
Why it's wrong here
D is wrong because row-level security filters rows, not columns, and cannot mask PII values within rows.
- ✗
Create an authorized view that does not include the PII columns and grant analysts access to the view.
Why it's wrong here
A is wrong because authorized views are static; adding new columns requires view updates, and aggregate queries on non-PII columns are fine, but view selection is limited.
- ✓
Use BigQuery column-level security with data masking to mask the PII columns for the analysts' role.
Why this is correct
C is correct because column-level masking dynamically masks data based on user permissions without changing the table structure.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between row-level security (filtering rows) and column-level security (masking or hiding columns), and candidates mistakenly choose row-level security when the requirement is to hide specific column values across all rows.
Detailed technical explanation
How to think about this question
BigQuery column-level security with data masking works by attaching a masking policy to a column via `CREATE OR REPLACE MASKING POLICY`; the policy can use functions like `CAST(column AS STRING)` or `SHA256(column)` to transform data at query time for users without the `EXEMPT_ACCESS_MASKING` privilege. A subtle behavior is that masking policies apply only to direct column access — aggregate functions like `COUNT` or `SUM` on masked columns still return accurate results because the masking is applied after aggregation, but `SELECT *` or direct column references will show the masked value. In a real-world scenario, this allows a data engineer to grant analysts `bigquery.dataViewer` on a table with a masking policy on `ssn` and `email`, so analysts can compute `COUNT(DISTINCT ssn)` for deduplication without ever seeing the raw SSNs.
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
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing 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 BigQuery column-level security with data masking to mask the PII columns for the analysts' role. — Option D is correct because BigQuery column-level security with data masking allows you to define masking policies on specific PII columns (e.g., using `DEFAULT_MASKING_RULE` or custom policies) that automatically transform the data for analysts' roles while still permitting aggregate queries over the entire dataset. This approach ensures analysts never see individual PII values, yet they can run `COUNT`, `SUM`, `AVG`, etc., on the masked columns, meeting both requirements precisely.
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.
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Last reviewed: Jun 30, 2026
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.
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