Databricks Certified Generative AI Engineer - Associate

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.