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Answer: All of the above.
When implementing a data masking and redaction strategy in Azure Synapse Analytics, it is important to consider several best practices. Identifying and classifying sensitive data (Option A) based on data privacy regulations and business requirements helps determine the appropriate masking and redaction techniques. Using dynamic data masking (Option B) protects sensitive data at the query level, ensuring that only authorized users can access the full data. Implementing data redaction techniques (Option C) such as tokenization, encryption, or format preserving encryption protects sensitive data at rest, preventing unauthorized access. Therefore, all of these best practices should be considered, making Option D the correct answer.
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You are working on a data analytics project that requires the use of Azure Synapse Analytics. Your team has identified a need to implement a data masking and redaction strategy to protect sensitive data. What are some of the best practices to consider when implementing data masking and redaction?
A
Identify and classify sensitive data based on data privacy regulations and business requirements.
B
Use dynamic data masking to protect sensitive data at the query level, allowing only authorized users to access the full data.
C
Implement data redaction techniques such as tokenization, encryption, or format preserving encryption to protect sensitive data at rest.
D
All of the above.
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