A Snowflake administrator needs to load data from multiple compressed (gzip) CSV files located in an external stage into a Snowflake table. Which *COPY INTO' command options can improve the loading pe

Question

Options

• ASpecifying = CASE_INSENSITIVE to handle potential case differences in column names.
• BUsing a larger virtual warehouse size during the load operation.
• CUtilizing to split large files before loading.
• DSetting ERROR = 'SKIP FILE" to ignore files with errors, thus preventing load interruption.
• EEnsuring the files are automatically compressed with GZIP and Snowflake automatically

• A
  13% (4)
• B
  78% (25)
• C
  3% (1)
• D
  6% (2)

Explanation

Using a larger virtual warehouse (B) improves performance because Snowflake distributes file loading across compute nodes in parallel - more nodes means more files processed simultaneously, directly reducing load time. GZIP-compressed files (E) reduce the volume of data transferred over the network and read from storage, which lowers I/O and bandwidth consumption; Snowflake automatically detects and decompresses GZIP without extra configuration.

Why the distractors are wrong:

• A (CASE_INSENSITIVE): This is a pattern-matching option for file path filtering, not a performance or resource optimization setting.
• C (split large files): Splitting files is a valid pre-processing best practice, but it is not a COPY INTO command option - it must be done before staging the files.
• D (ON_ERROR = 'SKIP_FILE'): This controls error-handling behavior to avoid load interruption, but it has no effect on speed or resource consumption - it just skips bad files.

Memory tip: Think "Big & Zipped" - a Big warehouse (more parallelism) and Zipped files (less data to move) are the two levers that directly affect how fast and how cheaply a bulk load runs. Everything else (case sensitivity, error handling) is about correctness or behavior, not efficiency.

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