ARA-C01 Question 328
Single answerSnowparkA data engineering team is rebuilding a Python-based feature engineering pipeline in Snowflake to reduce data movement and improve governance. The current process extracts large tables to an external Spark cluster, applies joins, filters, and aggregations, then writes the results back to Snowflake. The architect proposes using Snowpark for Python instead. The team must keep transformations scalable, minimize data egress, and avoid row-by-row processing patterns that would hurt performance. Which approach should the architect recommend?
- A
Use Snowpark DataFrame transformations so the joins, filters, and aggregations are expressed in Python but executed inside Snowflake, and only collect results to the client when necessary.
- B
Use Snowpark to fetch the source tables into local pandas DataFrames first, perform the transformations in the client application, and then write the final result back to Snowflake.
- C
Convert the pipeline to Python stored procedures that iterate through each row in Snowflake tables, because Snowpark is optimized for procedural row-by-row operations.
- D
Export the source data to an internal stage, process it in an external Python runtime, and reload the transformed data, because Snowpark cannot push DataFrame operations down to Snowflake.
Show answer and explanation
Correct answer: A
Explanation
Snowpark is designed to let developers use familiar programming languages such as Python to build data pipelines while executing the work in Snowflake. In practice, this means architects should favor Snowpark DataFrame operations for joins, filters, projections, and aggregations so computation stays close to the data and benefits from Snowflake optimization and governance controls. A key best practice is to avoid unnecessary use of collect() or conversion to local pandas DataFrames for large datasets, because that moves data out of Snowflake and reduces scalability. Likewise, row-by-row logic is generally less efficient than set-based transformations. Snowflake documentation for Snowpark emphasizes DataFrame-based development, pushdown execution, and minimizing client-side data retrieval except for small result sets or downstream local use cases.
- A. Correct.
Correct. Snowpark lets developers write transformations using a DataFrame API in Python while Snowflake compiles and executes the resulting logic inside the Snowflake engine. This aligns with the goals of minimizing data movement, keeping processing close to the data, and using scalable set-based operations instead of row-by-row patterns. Collecting results to the client should be limited to cases where a small result set is actually needed outside Snowflake.
- B. Incorrect.
Incorrect. Pulling large datasets into pandas on the client reintroduces the exact problem Snowpark is designed to reduce: data movement out of Snowflake. It also limits scalability to the client environment and can create governance and security concerns because data leaves Snowflake for transformation.
- C. Incorrect.
Incorrect. Python stored procedures can use Snowpark, but the statement that Snowpark is optimized for row-by-row procedural processing is false. Snowpark is most effective when used for declarative, set-based DataFrame transformations that Snowflake can optimize and execute in the engine. Iterating row by row is typically an anti-pattern for performance at scale.
- D. Incorrect.
Incorrect. Snowpark does support pushdown of DataFrame operations to Snowflake. Exporting data for external processing increases egress, orchestration complexity, and operational overhead. This option reflects a common misconception that Snowpark is only a client-side library rather than an interface for server-side execution in Snowflake.