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Question 10 of 103
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Select 3 answers.
This question asks for 3 answers, but our data marks only 1. Compare with the community discussion below for the full answer.
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F makes sense, but only once there has been a violation. I'd go with BDE, even though normalization across different datasets might not solve the problem.
B - Use IAM to define granular permissions. D - Only authorised users or systems can query or manipulate BigQuery data. E - By segregating data into different tables or datasets, specific permissions can be assigned to each data subset.
I disagree with [F]. It's too late for a "highly regulated industry" to detect access violations by audit logs. [E] is a more reasonable answer, since it is a kind of row-level security, especially the times when BigQuery row-level security wasn't available. It is a practice still recommended (even with row-level sec. available) for the extreme scenario that: (Through repeated observation of query duration when querying tables with row-level access policies,) "a user could infer the values of rows that otherwise might be protected by row-level access policies" "If you are sensitive to this level of protection, we recommend using separate tables to isolate rows with different access control requirements, instead." Source: https://cloud.google.com/bigquery/docs/best-practices-row-level-security#limit-side-channel-attacks