PDE Maintaining and Automating Data Workloads Practice Question
A company wants to use Cloud DLP to inspect data in BigQuery for sensitive information and de-identify it by masking credit card numbers. They want to perform this on a schedule. Which approach should they take?
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 Cloud DLP inspect and de-identify jobs triggered by Cloud Scheduler
Cloud DLP can inspect BigQuery tables and de-identify using transforms like masking. Scheduling can be done via Cloud Scheduler.
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 Dataplex data quality rules with a custom SQL regex
Why it's wrong here
Dataplex data quality rules evaluate conditions and report pass/fail; they cannot invoke Cloud DLP de-identification transforms to mask credit card numbers. It is tempting because Dataplex supports scheduled scans, but it would be correct for monitoring data quality metrics, not for inspecting and masking sensitive data.
- ✗
Use Cloud Data Loss Prevention API with Cloud Composer
Why it's wrong here
This option fails because Cloud DLP API with Cloud Composer necessitates building a custom workflow to iterate BigQuery tables and manage the de-identification process programmatically. It does not provide the direct, managed capability for *scheduled inspection jobs specifically targeting BigQuery tables* that the scenario requires. However, this approach is tempting as Composer orchestrates scheduled workflows, and the API offers granular control. It would be appropriate for highly bespoke data processing pipelines or inspecting data sources not natively supported by Cloud DLP's managed scan jobs.
- ✗
Use BigQuery column-level security with classification
Why it's wrong here
BigQuery column-level security uses policy tags to restrict who can read a column; it neither inspects values for sensitive data nor masks credit card numbers. It is tempting because it protects sensitive columns, but it would be correct when the requirement is access control on classified columns rather than scheduled DLP de-identification.
- ✓
Use Cloud DLP inspect and de-identify jobs triggered by Cloud Scheduler
Why this is correct
Cloud Scheduler triggers Cloud DLP inspect and de-identify jobs on a defined cadence, satisfying the scheduled requirement. De-identification uses the masking transformation to obscure credit card numbers while preserving data format. This combination inspects BigQuery data and applies masking without manual intervention, matching both the recurring schedule and de-identification constraints in the stem.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.