What challenges might arise when handling JSON data in Snowflake using built-in functions for traversing, flattening, and nesting? (Select all that apply)

Question

Options

• ARestrictions in data type conversions
• BDifficulty in handling nested data structures
• CComplexities in parsing and querying nested JSON objects
• DLimitations in processing specific JSON file sizes

• A
  9% (5)
• B
  88% (49)
• D
  4% (2)

Explanation

Snowflake's semi-structured data functions (like FLATTEN, GET_PATH, and PARSE_JSON) are powerful but come with real friction points: nested structures (B) require careful use of dot notation and bracket syntax to navigate deeply nested objects, and parsing/querying nested JSON (C) becomes complex when dealing with arrays of objects, mixed types, or inconsistent schemas - queries can quickly become verbose and error-prone.

Why A is wrong: Snowflake handles type casting from VARIANT columns quite flexibly using ::type syntax (e.g., value::STRING), so data type conversions aren't a notable restriction.

Why D is wrong: Snowflake has no meaningful file-size limitation specific to JSON processing at the query level - it's a cloud-scale platform designed for large data volumes, so this isn't a documented challenge with its built-in functions.

Memory tip: Think "structure and syntax" - the real JSON pain in Snowflake is always about shape (nested/hierarchical structures → B) and access (writing the right path expressions to query them → C). If a choice sounds like a generic SQL or storage limitation, it's likely a distractor.

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