DAA-C01 Question #74: Real Exam Question with Answer & Explanation

Question

How do partitioning strategies impact query performance and data storage efficiency in Snowflake?

Options

• APartitioning improves query planning only
• BEnhances query performance and reduces storage requirements
• CLimits data access for specific user roles
• DReduces query performance and increases storage requirements

Explanation

Option B is correct because partitioning in Snowflake - through its automatic micro-partitioning and clustering mechanisms - allows the query engine to skip irrelevant partitions via partition pruning, dramatically reducing the amount of data scanned and thus speeding up queries. Columnar storage combined with micro-partitions also compresses data efficiently, reducing physical storage footprint.

Why the distractors are wrong:

• A is too narrow - partitioning doesn't just aid query planning; it has a direct, measurable impact at execution time through data skipping.
• C is a red herring - access control is handled by Snowflake's RBAC/privilege system, not by partitioning.
• D is the opposite of reality - good partitioning improves performance and reduces storage via compression.

Memory tip: Think of micro-partitions like a well-organized filing cabinet. Instead of searching every drawer (full table scan), Snowflake reads the label on each drawer (partition metadata) and only opens the relevant ones - faster search, less wasted space.

No community discussion yet for this question.