Snowflake Certification Practice Test

What is the recommended method for storing semi-structured data in Snowflake?

1. Parse the semi-structure string into structured columns

2. Load semi-structured data as VARIANT data type

3. Convert to CSV before loading

4. Store as plain text

The correct answer is: Load semi-structured data as VARIANT data type

Storing semi-structured data as the VARIANT data type in Snowflake is recommended because VARIANT is specifically designed to accommodate semi-structured data formats such as JSON, Avro, Parquet, and XML. This data type allows for flexibility in handling the dynamic and potentially complex schema of semi-structured data, without the need to define a rigid structure in advance. By using the VARIANT data type, users can query and process the semi-structured data efficiently while taking advantage of Snowflake's powerful querying capabilities. This approach maintains the original structure of the data, allowing users to evolve their schemas as needed without the hassle of constant data transformation or reformatting. The other methods, such as converting to CSV or storing as plain text, do not provide the same level of flexibility and efficiency that VARIANT offers. Parsing the semi-structure string and breaking it down into structured columns also imposes additional constraints and requires upfront design choices that may hinder data adaptability and agility in handling changes to the data schemas over time. Thus, leveraging VARIANT is the optimal solution for managing semi-structured data within the Snowflake environment.