ARA-C01 Question 332
Single answerScalaA data engineering team is building a Snowpark application in Scala to standardize semi-structured event data stored in a VARIANT column. The transformation logic is complex, must run close to the data inside Snowflake, and needs to be reusable by SQL analysts in downstream queries. The architects want to minimize data movement and avoid managing external compute for the transformation step. Which approach best meets these requirements?
- A
Implement the transformation as a Snowpark Scala stored procedure and have analysts call it directly in SELECT statements wherever row-level parsing is needed.
- B
Implement the transformation as a Scala UDF using Snowpark so the row-level logic executes inside Snowflake and can be invoked from SQL queries.
- C
Extract the VARIANT data to an external Spark cluster, apply the Scala transformation there, and write the results back to Snowflake for analyst access.
- D
Use a JavaScript UDF because Scala code cannot run inside Snowflake, then orchestrate the logic from a Snowflake task.
Show answer and explanation
Correct answer: B
Explanation
When the requirement is to apply complex row-level transformation logic to data in Snowflake and make that logic reusable from SQL, a UDF is typically the correct architectural choice. With Snowpark, developers can write UDFs in Scala so the computation executes within Snowflake rather than on external infrastructure. This aligns with Snowflake best practices of pushing compute to the platform, minimizing data movement, and exposing reusable business logic to downstream users.
A stored procedure is better suited for procedural workflows, orchestration, and operations that may involve multiple statements or control flow, but not as an inline SQL expression for per-row transformations in analyst queries. External Spark would be appropriate only if there were requirements Snowflake could not meet, but that is not the case here. Snowflake documentation for Snowpark emphasizes in-platform development for UDFs and stored procedures, and Snowflake documentation on UDFs distinguishes their role from stored procedures for expression-level reuse in SQL.
- A. Incorrect.
Incorrect. Snowpark Scala stored procedures are useful for orchestration, multi-step data processing, and administrative workflows, but they are not designed to be invoked inline in a SELECT for row-by-row expression evaluation. Analysts needing reusable row-level parsing logic in SQL should use a UDF rather than a stored procedure.
- B. Correct.
Correct. A Scala UDF built with Snowpark is the best fit for reusable row-level transformation logic that should execute inside Snowflake. This keeps processing close to the data, avoids unnecessary data movement, and allows SQL analysts to call the function directly in queries. This is the standard pattern when the requirement is expression-style logic reusable from SQL.
- C. Incorrect.
Incorrect. Although Spark and Scala could perform the transformation, this approach violates the stated goal of minimizing data movement and avoiding external compute management. Moving data out of Snowflake for transformation adds operational overhead, latency, and architectural complexity when Snowpark can execute the logic within Snowflake.
- D. Incorrect.
Incorrect. Scala code can run in Snowflake through Snowpark for supported object types such as UDFs and stored procedures. Choosing JavaScript solely because of a belief that Scala cannot run in Snowflake is based on a false premise. Also, a task is for scheduling, not for replacing the need for the appropriate programmable object for row-level query reuse.