Snowflake SnowPro Certification Practice Test

How does Snowflake optimize the storage of semi-structured data?

1. By compressing all data equally

2. Through indexing every row

3. By handling repeated elements within the strings

4. By using external storage systems

The correct answer is: By handling repeated elements within the strings

Snowflake optimizes the storage of semi-structured data by effectively handling repeated elements within the strings. This approach is crucial because semi-structured data often contains nested structures or arrays that can include repeated values. By recognizing and managing these repeated elements, Snowflake can avoid unnecessary duplication in storage, which leads to more efficient use of space and improved query performance. Additionally, this optimization process allows for better compression mechanisms that work specifically with the characteristics of semi-structured data formats, such as JSON or Avro. By enabling better handling of nested and repeated structures, Snowflake ensures that semi-structured data can be stored and queried efficiently, aligning with the needs of modern data analytics and operational workloads. Other options are less applicable in this context. For instance, while general data compression is used, it does not specifically target the unique attributes of semi-structured formats. Indexing every row can also be intensive and is not a primary strategy for semi-structured data performance optimization. Finally, while external storage systems may be integrated, this is not the main method Snowflake uses to optimize semi-structured data storage; instead, it focuses on its internal handling capabilities.