When working with unfamiliar semi-structured data such as JSON, a common first step is to explore its structure and identify all possible keys. Snowflake's FLATTEN function is specifically designed to explode VARIANT, OBJECT, or ARRAY data into relational form. Using the RECURSIVE option allows FLATTEN to traverse nested objects and arrays, returning all nested keys regardless of depth (Answer A).
This approach enables architects to query and aggregate distinct key names, making it ideal for schema discovery and exploratory analysis. INFER_SCHEMA, by contrast, is used primarily with staged files to infer column definitions for external tables or COPY operations, not for exploring existing VARIANT data already stored in tables. PARSE_JSON simply converts a string into a VARIANT type and does not help identify keys. RESULT_SCAN is used to query the results of a previously executed query and is unrelated to schema discovery.
For SnowPro Architect candidates, this highlights an important semi-structured data design pattern: using FLATTEN (often with RECURSIVE) to explore, profile, and understand evolving data structures before committing to a relational schema or transformation pipeline.
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