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Answer: Configure the BigQuery dataset as multi-regional. Utilize point-in-time snapshots for data recovery in emergencies.
The optimal strategy combines multi-regional datasets for high availability and disaster recovery with point-in-time snapshots for cost-effective, precise recovery capabilities. Multi-regional datasets ensure data is replicated across multiple regions, enhancing availability. Point-in-time snapshots allow for incremental backups, storing only changes since the last snapshot, making them cost-effective and suitable for meeting a 30-day RPO. Other options either compromise on availability (regional datasets) or are less efficient and more complex (scheduled queries for data duplication).
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You have a critical dataset in BigQuery that requires high availability. What is the most cost-effective strategy to ensure a recovery point objective (RPO) of 30 days, while maintaining high availability?
A
Configure the BigQuery dataset as multi-regional. Implement scheduled queries to duplicate data into timestamp-suffixed tables for emergency backups.
B
Configure the BigQuery dataset as regional. Utilize point-in-time snapshots for data recovery in emergencies.
C
Configure the BigQuery dataset as multi-regional. Utilize point-in-time snapshots for data recovery in emergencies.
D
Configure the BigQuery dataset as regional. Implement scheduled queries to duplicate data into timestamp-suffixed tables for emergency backups.
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