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Answer: Utilizing Azure Synapse‘s dynamic data masking feature and accessing data via external tables in Databricks
The most suitable approach for enabling dynamic data masking within Azure Databricks when querying data stored in Azure Synapse Analytics is to utilize Azure Synapse‘s dynamic data masking feature and access data via external tables in Databricks. This method leverages Azure Synapse's built-in capabilities to apply data masking at the database level, ensuring sensitive data is masked when accessed from external tools like Azure Databricks. It simplifies the implementation by eliminating the need for custom masking logic in Databricks notebooks or complex runtime functions, thereby ensuring data security and compliance efficiently.
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How can dynamic data masking be implemented within Azure Databricks when querying data stored in Azure Synapse Analytics?
A
Implementing custom Scala or Python functions in Databricks for runtime data masking
B
Enabling dynamic data masking through Azure Active Directory conditional access policies
C
Configuring data masking rules directly in Databricks notebooks
D
Utilizing Azure Synapse‘s dynamic data masking feature and accessing data via external tables in Databricks
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