Which of the following statements are true regarding considerations when working with VARIANT data types in Snowflake? (Select all that apply)

Question

Options

• AQueries on VARIANT columns can be slower than queries on strongly-typed columns because
• BSnowflake automatically indexes VARIANT columns, eliminating the need for manual index
• CVARIANT columns can store data with different schema within the same column.
• DYou can use the operator to cast data extracted from a VARIANT column to a specific data type.
• EData stored in VARIANT columns always consumes less storage space than the equivalent data

• A
  56% (22)
• B
  31% (12)
• E
  13% (5)

Explanation

Options A, C, and D are correct because VARIANT columns in Snowflake are schema-flexible - they can hold semi-structured data (JSON, Parquet, etc.) with entirely different structures row-to-row (C), but this flexibility comes at a cost: Snowflake must parse and infer types at query time, making queries slower than on strongly-typed columns (A). To work around this, Snowflake provides the :: cast operator so you can explicitly cast extracted values to a known type, e.g., col:field::VARCHAR (D).

Option B is wrong because Snowflake does not automatically index VARIANT columns in a way that eliminates performance concerns - querying semi-structured paths still incurs overhead, which is precisely why A is true.

Option E is wrong because VARIANT storage can actually consume more space than equivalent strongly-typed columns due to the metadata overhead of storing structure information alongside values; the word "always" makes this definitively false.

Memory tip: Think "VARIANTs are flexible but costly" - flexible schema (C), castable with :: (D), but slower and potentially larger (ruling out B and E).

No community discussion yet for this question.