ARA-C01 Question 330
Single answerPythonA retail company uses Snowflake to process daily sales events. The architecture team wants data engineers to enrich incoming rows with product metadata by calling a third-party Python package inside Snowflake, while keeping data movement out of the client tier. The enrichment logic must be reusable in SQL pipelines and should run close to the data. A developer proposes several implementation approaches. Which approach best meets these requirements?
- A
Create a Python UDF or stored procedure in Snowflake that imports the required package from Snowflake-supported package sources or a staged artifact, and invoke it from SQL-based pipelines.
- B
Use a JavaScript UDF because JavaScript UDFs can directly import arbitrary Python libraries at runtime through the Snowflake execution engine.
- C
Run the Python package on an external application server, export Snowflake data to the server for enrichment, and reload the results because Snowflake SQL pipelines cannot invoke Python-based logic.
- D
Create a SQL UDF and specify the Python package in the function definition so Snowflake compiles the package into the SQL execution plan automatically.
Show answer and explanation
Correct answer: A
Explanation
The key architectural requirement is to execute reusable Python enrichment logic inside Snowflake and invoke it from SQL-driven workflows without exporting data to external systems. Snowflake addresses this with Python UDFs and Python stored procedures using Snowpark Python. These allow architects and engineers to keep processing in-platform, reduce data egress, and operationalize logic in ELT pipelines. In practice, the choice between a Python UDF and a Python stored procedure depends on the usage pattern: UDFs are appropriate for row-by-row or expression-style logic invoked in SQL, while stored procedures are better for orchestration and multi-step procedural tasks. Snowflake documentation on Snowpark Python, Python UDFs, and Python stored procedures describes dependency management, package support, and execution within Snowflake-managed runtimes.
- A. Correct.
Correct. Snowflake supports Python UDFs and Python stored procedures via Snowpark Python. These objects allow Python code to execute inside Snowflake, close to the data, which avoids unnecessary client-side data movement. Dependencies can be provided through supported package mechanisms, including package repositories available to Snowflake or staged artifacts, depending on the object type and deployment pattern. This is the best fit when the logic must be reusable from SQL pipelines and remain in-platform.
- B. Incorrect.
Incorrect. JavaScript UDFs execute JavaScript, not Python, and they cannot dynamically import arbitrary Python packages into the JavaScript runtime. This distractor reflects a common misconception that any procedural runtime in Snowflake can load libraries from another language.
- C. Incorrect.
Incorrect. While external applications can enrich data, this design violates the stated requirement to keep data movement out of the client tier and to run close to the data. It also adds operational complexity, network transfer, and orchestration overhead. Snowflake does support Python-based logic natively, so the premise in this option is false.
- D. Incorrect.
Incorrect. SQL UDFs are written in SQL and do not support embedding Python packages in their definitions. If Python package execution is required, the correct construct is a Python UDF or Python stored procedure, not a SQL UDF.