Snowflake SnowPro Certification Practice Test 2025 – Complete Exam Prep Guide

Using multiple small files when loading data into Snowflake allows for parallel processing by Virtual Warehouses, which enhances performance and efficiency. When data is divided into smaller files, Snowflake can concurrently load these files across different compute resources. This parallelism accelerates the overall loading process since multiple operations can be performed at the same time, reducing the time it takes to move data into the system.

The other options may seem appealing, but they do not accurately capture the primary advantage of using multiple small files in the context of Snowflake. Managing multiple files may not necessarily be easier, and smaller files do not inherently take up less storage space—the storage footprint is more related to the actual data size rather than file quantity. Furthermore, while using multiple small files can potentially improve loading times due to the parallel processing benefit, the primary and most consistent advantage of this approach is indeed the ability to leverage parallelism during data loading.