When handling Parquet files in Snowflake, what limitations or challenges might arise? (Select all that apply)

Question

Options

• AIssues in handling large Parquet files
• BRestrictions in metadata retrieval
• CDifficulties in accessing specific file types
• DConstraints in querying nested Parquet structures

• A
  94% (32)
• B
  3% (1)
• C
  3% (1)

Explanation

Snowflake presents real-world challenges when working with Parquet specifically around file size and nested data. Large Parquet files can strain Snowflake's stage-based ingestion pipeline, causing timeouts, memory pressure, or inefficient parallelism - best practice is to split files into smaller chunks (ideally 100–250 MB compressed). Querying nested or semi-structured Parquet data (arrays, structs, maps) requires careful use of lateral flattening and dot-notation path traversal, which adds complexity and can degrade performance compared to flat columnar data.

Options B and C are distractors: Snowflake does not impose special restrictions on metadata retrieval from Parquet beyond standard file format limits, and it broadly supports Parquet as a first-class file format with no unusual file-type access restrictions compared to other supported formats like ORC or Avro.

Memory tip: Think "Size and Structure" - the two S-problems with Parquet in Snowflake. Big files cause ingestion strain (Size = A), and deep nesting causes query pain (Structure = D).

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