When dealing with JSON data in Snowflake and utilizing built-in functions for traversing, flattening, and nesting, what complexities might arise? (Select all that apply)

Question

Options

• ADifficulties in parsing and querying nested JSON objects
• BComplexity in handling nested JSON structures
• CChallenges in handling specific data types only
• DLimitations in processing specific JSON data sizes

• A
  78% (21)
• C
  15% (4)
• D
  7% (2)

Explanation

Working with JSON in Snowflake using functions like FLATTEN, PARSE_JSON, and dot/bracket notation introduces genuine complexity around parsing nested objects (A) - deeply nested structures require chained path expressions or recursive flattening, making queries verbose and error-prone. Handling nested structures (B) is similarly complex because Snowflake's semi-structured data model requires understanding VARIANT type behavior, null propagation, and how lateral joins interact with FLATTEN across multiple nesting levels.

Option C is incorrect because Snowflake's VARIANT type handles a wide range of data types (strings, numbers, arrays, objects, booleans, nulls) - there's no meaningful limitation to specific types only. Option D is incorrect because Snowflake imposes a 16 MB limit per VARIANT value, but this is rarely a practical constraint and isn't considered a core complexity of JSON traversal and flattening specifically.

Memory tip: Think "A and B = Architecture and Branches" - the structure of JSON (how it's architecturally organized and how deeply it branches) is what creates real complexity in Snowflake, not data type restrictions or size limits.

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