The answer is de-identifying data using masking, tokenization, or pseudonymization, along with inspection and redaction of sensitive data in query results. Cloud DLP provides these three core capabilities for BigQuery by first scanning tables with built-in infoType detectors to classify sensitive elements like credit card numbers or PII, then applying transformations such as masking or tokenization to de-identify the data at rest or in query results, and finally redacting matched values before returning them to the user. On the Google Professional Cloud Security Engineer exam, this question tests your understanding of how Cloud DLP integrates directly with BigQuery’s query pipeline, not just as a separate scanning tool. A common trap is confusing Cloud DLP’s ability to inspect and redact inline with BigQuery queries versus only performing batch jobs; the key is that redaction happens dynamically during query execution. Remember the mnemonic “IRD” for Inspect, Redact, De-identify to recall the three capabilities.
PCSE Ensuring data protection Practice Question
This PCSE practice question tests your understanding of ensuring data protection. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is implementing data loss prevention (DLP) for BigQuery. Which THREE capabilities are provided by Cloud DLP? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Redacting sensitive data in query results.
Option A is correct because Cloud DLP can inspect and redact sensitive data directly in BigQuery query results. When you configure a DLP job or use the DLP API with BigQuery, you can specify infoTypes to detect and then redact matching values before returning the results to the user, preventing exposure of sensitive information like credit card numbers or PII.
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.
✓
Redacting sensitive data in query results.
Why this is correct
DLP can redact sensitive data in real-time during queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Auditing all queries that access sensitive columns.
Why it's wrong here
Auditing is done via Cloud Audit Logs, not DLP.
✓
Classifying data using built-in infoTypes.
Why this is correct
DLP includes many infoTypes for automatic classification of sensitive data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Encrypting entire tables with customer-managed keys.
Why it's wrong here
Table encryption is done via Cloud KMS or BigQuery CMEK, not DLP.
✓
De-identifying data using masking, tokenization, or pseudonymization.
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 DLP's de-identification capabilities and BigQuery's native encryption or auditing features, so candidates mistakenly select options like auditing or CMEK because they associate them with data protection, but DLP does not handle those functions.
Detailed technical explanation
How to think about this question
Cloud DLP uses built-in infoTypes (e.g., EMAIL_ADDRESS, CREDIT_CARD_NUMBER) to classify data via pattern matching, context analysis, and machine learning. For de-identification, it supports techniques like masking (e.g., replacing characters with 'X'), tokenization (replacing sensitive values with a token stored in a separate mapping), and pseudonymization (reversible cryptographic transformation). In a real-world scenario, a healthcare company could use DLP to redact patient SSNs from BigQuery query results while preserving the ability to re-identify data for authorized analysts via pseudonymization.
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.
Ensuring data protection — This question tests Ensuring data protection — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Redacting sensitive data in query results. — Option A is correct because Cloud DLP can inspect and redact sensitive data directly in BigQuery query results. When you configure a DLP job or use the DLP API with BigQuery, you can specify infoTypes to detect and then redact matching values before returning the results to the user, preventing exposure of sensitive information like credit card numbers or PII.
What should I do if I get this PCSE 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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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.
Question Discussion
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