In Snowflake, how does leveraging Parquet format contribute to optimizing query performance and storage efficiency?

Question

Options

• ALimits query execution time
• BImpedes data compression and decompression capabilities
• CParquet format accelerates only metadata retrieval
• DEnhances query performance and reduces storage requirements

• A
  3% (1)
• B
  6% (2)
• C
  3% (1)
• D
  89% (31)

Explanation

D is correct because Parquet is a columnar storage format that allows Snowflake to read only the specific columns a query needs (rather than entire rows), dramatically reducing I/O. Its built-in compression algorithms (like Snappy or GZIP) also shrink file sizes on disk, delivering both faster queries and lower storage costs simultaneously.

Why the distractors fail:

• A is too narrow - Parquet doesn't impose a limit on execution time; it improves performance but doesn't cap or guarantee it.
• B is the opposite of reality - Parquet actively enables efficient compression and fast decompression, not impedes it.
• C is a half-truth trap - Parquet does store rich metadata (column statistics, min/max values) that speeds up predicate pruning, but its benefits extend far beyond metadata to full columnar scan and compression advantages.

Memory tip: Think of Parquet as a "column-first filing cabinet" - you only pull the drawer you need, and everything inside is vacuum-packed. Less searching + less space = better performance + lower cost. That maps directly to option D.

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