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Answer: All of the above - By combining these functions, you can create dynamic views that perform comprehensive data masking tailored to user roles and permissions, ensuring compliance and data utility.
The correct answer is D, 'All of the above', because each of these SQL functions plays a unique role in data masking within Databricks. The CASE WHEN function provides the flexibility to apply conditional logic for masking based on specific conditions. The IF ELSE function can handle simpler conditional checks, and the COALESCE function is effective for masking NULL values. Together, they enable the creation of dynamic views that can perform sophisticated data masking based on user roles and permissions, ensuring compliance with GDPR and other regulations while maintaining data utility for analytics purposes. This approach also aligns with the goal of minimizing operational costs by leveraging existing SQL functionalities within Databricks.
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In a scenario where you are tasked with implementing data masking for a dataset containing sensitive information in Databricks, you need to ensure compliance with GDPR and minimize operational costs while maintaining data utility for analytics. Considering these constraints, which of the following SQL functions would be the most appropriate to use for dynamic data masking based on user roles and permissions? Choose the best option.
A
CASE WHEN - Allows for conditional logic to mask data based on specific conditions but may require complex queries for dynamic role-based masking.
B
IF ELSE - Suitable for simple conditional checks to mask data but lacks the flexibility for complex role-based scenarios without extensive scripting.
C
COALESCE - Useful for replacing NULL values with a default value, thus masking NULLs, but does not address the need for dynamic masking based on user roles.
D
All of the above - By combining these functions, you can create dynamic views that perform comprehensive data masking tailored to user roles and permissions, ensuring compliance and data utility.