A data engineer is tasked with loading JSON data representing customer interactions into Snowflake. The JSON files contain varying schemas and nested arrays. To optimize query performance and minimize

Question

Options

• ALoad the JSON data directly into a VARIANT column without any transformations and use
• BCreate a relational schema based on the most common JSON structure and load only those fields
• CUse a CREATE VIEW statement to flatten and transform the VARIANT column into a relational
• DUtilize Snowflake's schema detection feature during loading to automatically create a relational
• EParse and pre-process the JSON data outside Snowflake to create a consistent relational structure

• A
  11% (2)
• B
  33% (6)
• C
  6% (1)
• D
  44% (8)
• E
  6% (1)

Explanation

Snowflake's schema detection feature (INFER_SCHEMA) automatically scans staged JSON files and derives typed column definitions, enabling you to create a fully relational table via CREATE TABLE ... USING TEMPLATE. This converts semi-structured data into Snowflake's native columnar format at load time, unlocking micro-partition pruning, compression, and direct column access - all of which optimize both query performance and storage costs simultaneously.

Why the distractors fail:

• A (VARIANT column, no transformation) stores data as opaque semi-structured blobs; querying requires slower path expressions (data:field::type) and foregoes columnar compression benefits.
• B (manually design schema from common structure) discards fields from less-common JSON shapes, causing data loss, and requires error-prone manual schema design that schema detection automates.
• C (CREATE VIEW over VARIANT) is purely logical - it runs the flattening transformation at query time on every execution, providing zero storage savings and no persistent performance gain.
• E (pre-process outside Snowflake) adds pipeline complexity and external dependencies when the same result is achievable natively, violating the principle of using platform-native tooling.

Memory tip: Think "D = Detect once, query fast forever" - schema detection does the heavy lifting at load time so every subsequent query pays the cheap columnar access cost, not the expensive JSON-parsing cost.

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