A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data Engineer needs to move these transformations to Snowpark. What characteristics of data…

Question

Options

• AIt is possible to join multiple tables using DataFrames.
• BSnowpark operations are executed lazily on the server.
• CUser-Defined Functions (UDFs) are not pushed down to Snowflake.
• DSnowpark requires a separate cluster outside of Snowflake for computations.
• EColumns in different DataFrames with the same name should be referred to with squared

• A
  91% (31)
• C
  6% (2)
• D
  3% (1)

Explanation

A (joining multiple tables via DataFrames) is correct because Snowpark's DataFrame API mirrors Spark's - you can join, filter, aggregate, and union across multiple DataFrames, making Scala-based transformation logic portable. B (lazy evaluation on the server) is correct because Snowpark operations are not executed immediately; they build a logical query plan that is only sent to Snowflake when an action (like .collect() or .show()) is called - this is the same lazy/deferred model Spark developers already know.

C is wrong - UDFs in Snowpark are pushed down and executed inside Snowflake, which is a core performance advantage. D is wrong - Snowpark runs compute entirely within Snowflake's existing virtual warehouses; no external cluster is needed. E is a distractor - column ambiguity in Snowpark is resolved using the DataFrame reference directly (e.g., df1["col"]), not bracket/squared notation as the incomplete option implies.

Memory tip: Think "SLIP into Snowpark" - Server-side execution, Lazy evaluation, and the ability to Join and Process DataFrames just like Spark. If an option says Snowpark needs external resources or limits pushdown, it's wrong - Snowpark's whole value is keeping everything inside Snowflake.

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