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Cloud DLP for BigQuery — Inspection, Classification, and Deidentification
Which THREE of the following are best practices for using Cloud DLP to protect sensitive data in BigQuery?
Quick Answer
The answer is to automatically apply deidentification transformations to sensitive columns. This is correct because Cloud DLP inspection jobs first scan BigQuery tables to classify sensitive data types like PII or credit card numbers using built-in infoTypes, and then deidentification transformations such as masking or tokenization can be applied directly to those columns to protect the data at rest. On the Google Professional Cloud Security Engineer exam, this tests your understanding of the full data protection workflow—inspection, classification, and deidentification—as a continuous pipeline rather than isolated steps. A common trap is assuming that simply classifying data is sufficient, but the exam emphasizes that deidentification must be actively applied to reduce risk. Remember the memory tip: “Inspect, classify, then deidentify—never leave sensitive data unguarded.”
⚠ Common exam trap
Google Cloud often tests the distinction between data discovery/inspection (DLP) and data protection mechanisms like encryption (CMEK) or access control (IAM), leading candidates to confuse complementary security controls with DLP-specific best practices.
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 inspection jobs to scan tables and identify sensitive data.
Cloud DLP inspection jobs can scan BigQuery tables to identify sensitive data types (e.g., PII, credit card numbers) using built-in infoTypes. This is a foundational step before applying any deidentification or access control measures, as it allows you to discover where sensitive data resides.
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 inspection jobs to scan tables and identify sensitive data.
Why this is correct
Inspecting data is a foundational step for data protection.
- ✗
Store transformed data in a separate dataset with stricter access controls.
Why it's wrong here
This is an architectural choice, not a DLP best practice.
- ✓
Use DLP to classify data and then apply access controls based on data classification.
Why this is correct
Classification-based access is a recommended approach.
- ✓
Automatically apply deidentification transformations to sensitive columns.
Why this is correct
Automation ensures consistent protection.
- ✗
Encrypt the entire BigQuery table using CMEK.
Why it's wrong here
Encryption is important but not a DLP-specific best practice.
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Variation 1. An organization uses Cloud DLP to inspect BigQuery tables for sensitive data. They want to automatically remove credit card numbers before the data is accessed by analysts but still allow the raw data for auditing purposes. Which DLP technique should they use?
easy- A.De-identification transform with masking
- B.Re-identification transform with tokenization
- ✓ C.De-identification transform with redact
- D.De-identification transform with replacement
Why C: The 'De-identification transform with redact' completely removes sensitive data (like credit card numbers) from the output, ensuring analysts never see the raw values while the original data remains intact in the source BigQuery table for auditing. This aligns with the requirement to automatically strip sensitive content before access, without altering the stored data.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.