How does leveraging partition pruning optimize query performance in Snowflake?

Question

Options

• ALimits query complexity and optimization possibilities
• BReduces data accessibility across multiple warehouses
• CIncreases storage requirements for optimized query access
• DFilters unnecessary partitions during query execution

• B
  6% (2)
• C
  3% (1)
• D
  92% (33)

Explanation

Partition pruning optimizes Snowflake query performance by filtering out irrelevant micro-partitions before scanning, so the query engine only reads data that could satisfy the WHERE clause conditions - directly making D correct. This dramatically reduces I/O and speeds up execution on large tables without any manual indexing.

Why the distractors are wrong:

• A is backwards - pruning expands optimization possibilities by reducing the data the optimizer must process.
• B is a fabrication - pruning has nothing to do with cross-warehouse data accessibility; it's a storage-layer optimization.
• C is the opposite of reality - pruning reduces the data read, it doesn't increase storage requirements.

Memory tip: Think of partition pruning like a library index - instead of reading every book (partition) to find what you need, you skip directly to the relevant shelf. "Prune = cut away the unnecessary."

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