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Answer: Access historical data by using time travel in BigQuery.
**Correct Answer: C. Access historical data by using time travel in BigQuery.** - **Time travel** in BigQuery allows querying data as it was at any point in the past seven days, enabling recovery from corruption by accessing pre-corruption data. - It provides a **low Recovery Point Objective (RPO)** by allowing access to data states before corruption. - As a **managed service**, it's **cost-effective** compared to manual exports or migrations. **Why other options are incorrect:** - **A. Migrate your data to multi-region BigQuery buckets:** Enhances redundancy but doesn't directly address corruption recovery within the specified timeframe. - **B. Create a BigQuery table snapshot on a daily basis:** Good for backups but lacks the flexibility to recover data from any point within the last seven days. - **D. Export the data from BigQuery into a new table that excludes the corrupted data:** Doesn't ensure fault tolerance or provide a mechanism to access historical data states.
Author: LeetQuiz Editorial Team
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You are designing a fault-tolerant architecture for a regional BigQuery dataset to recover from data corruption within the last seven days. Which managed service solution offers the lowest Recovery Point Objective (RPO) and is cost-effective?
A
Migrate your data to multi-region BigQuery buckets.
B
Create a BigQuery table snapshot on a daily basis.
C
Access historical data by using time travel in BigQuery.
D
Export the data from BigQuery into a new table that excludes the corrupted data.
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