Answer: Dynamic masking at the view level

## Explanation **Dynamic masking at the view level** ensures that sensitive information is masked based on user permissions, providing real-time governance without altering the underlying data. This allows for flexibility in data access while still enforcing security policies. **Caching frequently queried results** helps optimize performance by reducing the need to reapply masking operations every time a query is executed. This approach ensures that data governance is maintained while also improving query performance. ### Why not the other options? - **A.** Column-level encryption and static masking ensure data security but might negatively impact performance, especially in real-time analytics, due to the overhead of encryption and decryption. - **C.** Masking data at the storage layer can be effective, but it lacks flexibility and might not provide the real-time dynamic masking required for different user roles and permissions. - **D.** Query-level dynamic masking can ensure governance but could lead to performance bottlenecks if masking operations are repeated for every query, particularly in a high-frequency, real-time analytics environment. Thus, **B provides the best balance between data governance and query performance in Databricks**, leveraging dynamic masking and caching.

Author: LeetQuiz .