For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?
A. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
B. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
C. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36months.
D. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
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C is Correct: https://cloud.google.com/bigquery/docs/best-practices-storage
C is correct.
A is correct.
Explanation: If we will use time-partitioned Bigquery tables then there are chances that the data which has just arrived gets deleted because the tables age has passed 36 months.
There’s no such thing as “table retention” in bigquery. You create and set table expirations. C. Makes the most sense here.
Answer should be C – Time partitioning to support data expiry in BQ and GSUtil with Lifecycle set of delete in 36 months..