You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do? 搒treet?appears.

Question

Options

• AWrite a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word
• BCreate a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the
• CCreate a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the
• DCreate a de-identification job in Cloud Data Loss Prevention and use the masking transformation.

• A
  13% (2)
• B
  80% (12)
• C
  7% (1)

Explanation

Option B is correct because Cloud Data Loss Prevention (Cloud DLP) deep inspection jobs are purpose-built to scan specific BigQuery tables for sensitive data infoTypes - including STREET_ADDRESS - and return all matching findings. This is the right tool when you have a known dataset and need to locate PII at scale, without writing custom logic.

Option A is wrong because REGEXP_CONTAINS requires you to manually write regex patterns covering every possible street address format - highly error-prone and impractical when DLP has a built-in STREET_ADDRESS infoType that handles this reliably.

Option C is wrong because a discovery scan configuration operates at the organization level and is designed for automated, ongoing data classification across unknown data assets - not targeted inspection of a specific dataset you already know about. It's the wrong scope for this task.

Option D is wrong because de-identification jobs mask or transform sensitive data (e.g., replacing addresses with [REDACTED]); the question asks to retrieve/find occurrences, not remove them.

Memory tip: Match the tool to the task scope - Deep inspection job = targeted scan of known tables to find sensitive data; Discovery scan = org-wide sweep to catalog where sensitive data lives; De-identification = transform data you've already found.

No community discussion yet for this question.