How does leveraging window functions in Snowflake differ from using table functions for data manipulation?

Question

Options

• ATable functions generate tables as output
• BTable functions are limited to specific data types only
• CWindow functions operate on entire datasets
• DWindow functions modify table structures directly

• A
  3% (1)
• B
  8% (3)
• C
  87% (33)
• D
  3% (1)

Explanation

Window functions in Snowflake operate across a defined partition or the entire dataset without collapsing rows, meaning each input row retains its identity in the output while gaining access to aggregate or ranking computations over a surrounding window - this is what makes option C correct. Table functions, by contrast, are invoked in the FROM clause and return a set of rows as a virtual table, making option A a true statement about table functions (not a distinguishing flaw), but it doesn't speak to how they differ from window functions in data manipulation. Option B is false because table functions like FLATTEN or GENERATOR handle multiple data types, including semi-structured data such as JSON - they are not type-restricted. Option D is false because window functions are purely read-time computations; they never alter table schemas or structures.

Memory tip: Think of window functions as "looking through a window at your neighbors" - each row sees its context but stays in its seat. Table functions are "factories that produce new tables." If rows stay put but gain new computed columns, it's a window function.

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