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Answer: Using Databricks' Optimized Writes to Minimize Performance Impact of Masking, Creating Materialized Views with Masking Logic Pre-applied
**Correct Answers: C and E** **Explanation:** - **C. Using Databricks' Optimized Writes to Minimize Performance Impact of Masking** Optimized writes help reduce the overhead involved in writing data, ensuring that the performance impact of applying data masking is minimized while still securing sensitive information. - **E. Creating Materialized Views with Masking Logic Pre-applied** By creating materialized views that have masking logic applied, you can ensure sensitive information is protected while maintaining optimal query performance. The views precompute the masked data, making reporting queries faster. **Why other options are incorrect:** - **A:** Avoiding masking defeats the security objective of protecting sensitive information - **B:** Z-Order indexing helps with query performance but doesn't directly address the masking performance impact - **D:** Pass-through authentication is about identity management, not data masking performance optimization
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Question: 6
You are working with a large-scale dataset on Databricks that includes personal information. You are required to mask sensitive information while ensuring query performance remains optimal for reporting. Which of the following techniques should you consider to meet both security and performance objectives? (Select two)
A
Implementing Fine-Grained Access Control and avoiding masking to improve performance
B
Leveraging Databricks' Z-Order Indexing to Speed Up Queries with Masking
C
Using Databricks' Optimized Writes to Minimize Performance Impact of Masking
D
Using Pass-Through Authentication to Ensure Performance with Masked Data
E
Creating Materialized Views with Masking Logic Pre-applied