DAA-C01 Question #153: Real Exam Question with Answer & Explanation

Question

When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?

Options

• ACSV format enhances query performance and minimizes storage needs
• BParquet format optimizes query performance and reduces storage requirements
• CAll formats have similar impacts on query performance and storage
• DJSON format accelerates query execution but increases storage requirements

Explanation

Parquet is a columnar storage format purpose-built for analytics workloads: it enables column pruning (reading only the columns a query needs), supports efficient compression algorithms, and stores metadata like min/max values per column to enable predicate pushdown - all of which dramatically reduce both I/O and storage costs in Snowflake.

Why the distractors are wrong:

• A (CSV): CSV is a flat, row-oriented text format with no compression or metadata - it's the worst performer for query optimization and consumes more storage than binary formats.
• C (all formats similar): Meaningfully false - format choice is one of the most impactful decisions for query performance and storage in Snowflake; Parquet consistently outperforms both CSV and raw JSON.
• D (JSON accelerates queries): JSON is semi-structured and schema-less, which adds parsing overhead at query time and stores redundant key names with every record, increasing storage rather than reducing it.

Memory tip: Think of Parquet as a pre-organized filing cabinet - data is sorted by column, pre-compressed, and indexed. CSV and JSON are like loose papers in a pile - you have to read everything to find anything.

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