DEA-C02 Data Movement Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Snowflake only processes one file at a time, regardless of the warehouse size.

  Why it's wrong here

  Snowflake is designed for massively parallel processing. During a COPY INTO operation, it automatically distributes files across the nodes in the virtual warehouse to perform concurrent loading. Larger warehouses have more nodes, allowing for even greater parallelism and faster load times for high-volume data movement tasks.

• ✗

  The files must be manually split by the engineer before the COPY INTO command is executed.

  Why it's wrong here

  Manual splitting of files is unnecessary and inefficient. Snowflake's load engine is capable of reading multiple files simultaneously and can also handle large files by splitting the work internally. The engineer simply needs to point to the stage, and Snowflake handles the parallel partitioning of the workload automatically.

• ✓

  Snowflake automatically parallelizes the load by distributing files across the nodes of the virtual warehouse.

  Why this is correct

  Snowflake automatically detects the number of files in the specified path and distributes them across available nodes in the virtual warehouse. This parallel processing capability is fundamental to Snowflake's architecture, enabling rapid ingestion of large datasets without the need for complex, manual partitioning logic on the part of the engineer.

• ✗

  Partitioning is only supported when loading data from internal stages.

  Why it's wrong here

  Parallel loading is supported for both internal and external stages. Snowflake does not discriminate based on the stage type when performing parallel operations. Whether the data resides in a local internal stage or a cloud-based external bucket, the engine optimizes the ingest process through parallel file distribution.