A Snowflake administrator observes slow query performance when querying data loaded from a large set of compressed CSV files stored in an external stage (AWS S3). Which of the following actions, perfo

Question

Options

• AIncrease the virtual warehouse size used for querying.
• BCreate a materialized view that pre-aggregates the data from the CSV files.
• CConfigure the external stage to use the IAM role.
• DCreate a file format object that specifies the compression type used in the CSV files.
• EPartition the data in S3 based on a commonly used filter column and update the stage definition to

• A
  68% (19)
• B
  18% (5)
• C
  11% (3)
• E
  4% (1)

Explanation

Increasing warehouse size (A) directly adds parallel compute resources, allowing Snowflake to scan and process more CSV files simultaneously from the external stage - this is the most immediate performance lever available. Creating a file format object specifying the compression type (D) ensures Snowflake knows exactly how to decode each file without auto-detection overhead, enabling efficient, optimized reads from S3.

Why the distractors are wrong:

• B - Materialized views cannot be created over external stage data (raw S3 files); they require data in Snowflake tables.
• C - Using an IAM role is a security/authentication best practice, but it has no impact on query execution speed.
• E - S3 partitioning with partition pruning benefits external tables (which have defined partition metadata), not a plain external stage; the stage definition alone cannot enable partition elimination.

Memory tip: Think "Scale the engine, spell out the format." When querying staged files, Snowflake needs enough horsepower (warehouse size) and clear instructions (file format with compression) - both address the read bottleneck directly, while the other options touch security, views, or infrastructure that don't fix the core I/O problem.

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