- A
Enable customer-managed encryption keys (CMEK) to encrypt sensitive columns.
Correct. CMEK encrypts data at rest, protecting sensitive columns from unauthorized access to storage layers.
- B
Apply Cloud DLP de-identification transforms during data ingestion.
Correct. Cloud DLP de-identification transforms can mask, tokenize, or redact sensitive data before it is stored in BigQuery.
- C
Create authorized views that query only non-sensitive columns.
Why wrong: Incorrect. Authorized views restrict access to rows or columns but do not encrypt or de-identify data; they are an access control mechanism, not a direct data protection method.
- D
Use BigQuery column-level security to restrict access to sensitive columns.
Correct. Column-level security (Policy Tags) restricts access to sensitive columns by authorized users only.
- E
Use IAM roles to grant access at the dataset level, which automatically masks sensitive data.
Why wrong: Incorrect. IAM roles grant access at the dataset level but do not automatically mask sensitive data; they control overall access, not data masking.
Three Valid Methods to Protect Sensitive Data in BigQuery
This PCA practice question tests your understanding of design for security and compliance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: customer-managed encryption keys (CMEK). 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.
Which THREE are valid methods to protect sensitive data in BigQuery?
Quick Answer
The answer is to use BigQuery column-level security to restrict access to sensitive columns. This method works by applying access policies directly to specific columns within a table, allowing you to control which users or roles can view sensitive data like PII or financial records without duplicating tables or creating views. On the Google Professional Cloud Architect exam, this question tests your understanding of data governance and access control within BigQuery, often appearing alongside topics like Cloud DLP de-identification transforms and authorized views. A common trap is confusing row-level security with column-level security—remember that column-level security protects vertical slices of data, while row-level security filters horizontal rows. For the exam, pair this with Cloud DLP during ingestion to automatically mask or tokenize sensitive data before it reaches storage, ensuring protection at rest. Memory tip: think "columns for categories, rows for conditions" to keep the distinction clear.
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
Enable customer-managed encryption keys (CMEK) to encrypt sensitive columns.
Customer-managed encryption keys (CMEK) provide encryption at rest for BigQuery data, including sensitive columns, by using keys managed by the customer. Cloud DLP de-identification transforms can be applied during data ingestion to automatically mask or tokenize sensitive data. BigQuery column-level security allows restricting access to sensitive columns at the table level. These three methods directly protect sensitive data in BigQuery.
Key principle: Customer-managed encryption keys (CMEK)
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable customer-managed encryption keys (CMEK) to encrypt sensitive columns.
Why this is correct
Correct. CMEK encrypts data at rest, protecting sensitive columns from unauthorized access to storage layers.
Related concept
Customer-managed encryption keys (CMEK)
- ✓
Apply Cloud DLP de-identification transforms during data ingestion.
Why this is correct
Correct. Cloud DLP de-identification transforms can mask, tokenize, or redact sensitive data before it is stored in BigQuery.
Related concept
Customer-managed encryption keys (CMEK)
- ✗
Create authorized views that query only non-sensitive columns.
Why it's wrong here
Incorrect. Authorized views restrict access to rows or columns but do not encrypt or de-identify data; they are an access control mechanism, not a direct data protection method.
- ✓
Use BigQuery column-level security to restrict access to sensitive columns.
Why this is correct
Correct. Column-level security (Policy Tags) restricts access to sensitive columns by authorized users only.
Related concept
Customer-managed encryption keys (CMEK)
- ✗
Use IAM roles to grant access at the dataset level, which automatically masks sensitive data.
Why it's wrong here
Incorrect. IAM roles grant access at the dataset level but do not automatically mask sensitive data; they control overall access, not data masking.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is to think that customer-managed encryption keys (CMEK) only protect entire tables and are not a valid method for sensitive data protection. While CMEK does not target specific columns, encryption at rest is a valid layer of protection for sensitive data. Another trap is to confuse authorized views (access control) with column-level security (data masking).
Detailed technical explanation
How to think about this question
Cloud DLP integrates with BigQuery via the Data Loss Prevention API, allowing you to define inspection templates and de-identification templates that can be applied during data ingestion using Cloud Dataflow or Cloud Functions. This enables real-time transformation of sensitive fields such as credit card numbers or social security numbers into formats like tokenized values or hashed strings, ensuring compliance with regulations like GDPR or PCI DSS.
KKey Concepts to Remember
- Customer-managed encryption keys (CMEK)
- Cloud DLP de-identification transforms
- BigQuery column-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
Customer-managed encryption keys (CMEK)
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 customer-managed encryption keys (CMEK), then practise related PCA questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PCA question test?
Design for security and compliance — This question tests Design for security and compliance — Customer-managed encryption keys (CMEK).
What is the correct answer to this question?
The correct answer is: Enable customer-managed encryption keys (CMEK) to encrypt sensitive columns. — Customer-managed encryption keys (CMEK) provide encryption at rest for BigQuery data, including sensitive columns, by using keys managed by the customer. Cloud DLP de-identification transforms can be applied during data ingestion to automatically mask or tokenize sensitive data. BigQuery column-level security allows restricting access to sensitive columns at the table level. These three methods directly protect sensitive data in BigQuery.
What should I do if I get this PCA question wrong?
Review customer-managed encryption keys (CMEK), then practise related PCA questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Customer-managed encryption keys (CMEK)
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Last reviewed: Jun 11, 2026
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