You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that…

Question

Options

• AAccess historical data by using time travel in BigQuery.
• BExport the data from BigQuery into a new table that excludes the corrupted data
• CCreate a BigQuery table snapshot on a daily basis.
• DMigrate your data to multi-region BigQuery buckets.

• A
  82% (23)
• B
  4% (1)
• C
  7% (2)
• D
  7% (2)

Explanation

BigQuery's time travel feature is a fully managed, zero-configuration capability that lets you query or restore table data to any point within the past 1–7 days (configurable window), giving you near-zero RPO with no extra storage cost - making it the most cost-effective and lowest-RPO option for recovering from a corruption event within that window.

B is wrong because manually exporting to a new table is a remediation step, not a fault-tolerant architecture; it has no defined RPO and is not a managed service. C is wrong because daily snapshots cap your RPO at 24 hours and incur additional storage charges, making it costlier and less precise than time travel. D is wrong because multi-region replication protects against regional outages, not data corruption - corrupted data replicates across regions just like valid data, so it solves the wrong problem entirely.

Memory tip: Think of time travel as BigQuery's built-in "Ctrl+Z" - free, automatic, no setup required, and it works anywhere within the 7-day window. If the question mentions corruption + 7 days + managed + cost-effective, time travel is almost always the answer.

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