How does the Access_History view enhance overall data governance pertaining to read and write operations? (Select TWO).
A.
Shows how the accessed data was moved from the source lo the target objects
A.
Shows how the accessed data was moved from the source lo the target objects
B.
Provides a unified picture of what data was accessed and when it was accessed
B.
Provides a unified picture of what data was accessed and when it was accessed
C.
Protects sensitive data from unauthorized access while allowing authorized users to access it at query runtime
C.
Protects sensitive data from unauthorized access while allowing authorized users to access it at query runtime
D.
Identifies columns with personal information and tags them so masking policies can be applied to protect sensitive data
D.
Identifies columns with personal information and tags them so masking policies can be applied to protect sensitive data
E.
Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy
E.
Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy
Suggested answer: B, E
Explanation:
The ACCESS_HISTORY view in Snowflake is a powerful tool for enhancing data governance, especially concerning monitoring and auditing data access patterns for both read and write operations. The key ways in which ACCESS_HISTORY enhances overall data governance are:
B . Provides a unified picture of what data was accessed and when it was accessed: This view logs details about query executions, including the objects (tables, views) accessed and the timestamps of these accesses. It's instrumental in auditing and compliance scenarios, where understanding the access patterns to sensitive data is critical.
E . Determines whether a given row in a table can be accessed by the user by filtering the data based on a given policy: While this option is a bit of a misinterpretation of what ACCESS_HISTORY directly offers, it indirectly supports data governance by providing the information necessary to analyze access patterns. This analysis can then inform policy decisions, such as implementing Row-Level Security (RLS) to restrict access to specific rows based on user roles or attributes.
ACCESS_HISTORY does not automatically apply data masking or tag columns with personal information. However, the insights derived from analyzing ACCESS_HISTORY can be used to identify sensitive data and inform the application of masking policies or other security measures.
References:
Snowflake Documentation on ACCESS_HISTORY: Access History
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