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Answer: Use a unique identifier for each individual. Upon a deletion request, delete all rows from BigQuery with this identifier.
The correct answer is A because it directly addresses the legislative requirement to delete personal health data upon request by using a unique identifier to locate and delete all associated rows in BigQuery. This ensures complete removal of the data, which is mandated by regulations like GDPR. Option B, while leveraging DLP API and Data Catalog for identifying sensitive data, does not explicitly include a deletion step and adds unnecessary complexity for a straightforward deletion requirement. Options C and D are unsuitable as they do not delete data; C only hides it via a view, and D merely obfuscates the identifier without removing the underlying health data. The community discussion shows a split, but the consensus among high-upvoted comments (e.g., upvotes of 12, 9, 8) favors A for its simplicity and compliance with deletion mandates, whereas B is criticized for not ensuring actual data deletion.
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You need to design a BigQuery solution that allows for the deletion of individual health records to comply with data privacy regulations requiring data removal upon a subject's request. What is your recommended approach?
A
Use a unique identifier for each individual. Upon a deletion request, delete all rows from BigQuery with this identifier.
B
When ingesting new data in BigQuery, run the data through the Data Loss Prevention (DLP) API to identify any personal information. As part of the DLP scan, save the result to Data Catalog. Upon a deletion request, query Data Catalog to find the column with personal information.
C
Create a BigQuery view over the table that contains all data. Upon a deletion request, exclude the rows that affect the subject's data from this view. Use this view instead of the source table for all analysis tasks.
D
Use a unique identifier for each individual. Upon a deletion request, overwrite the column with the unique identifier with a salted SHA256 of its value.