SOL-C01 · Question #3
You are using a Snowflake Notebook to perform data analysis on a large dataset. As part of your analysis, you need to create a custom Python function that calculates a complex metric based on multiple
The correct answer is C. Create a Snowflake Python User-Defined Function (UDF) that encapsulates the calculation logic. Creating a Snowflake Python UDF (option C) is correct because UDFs execute server-side within Snowflake's distributed compute engine, meaning the function runs in parallel across all rows without moving data out of Snowflake - this is both efficient and scales automatically with
Question
You are using a Snowflake Notebook to perform data analysis on a large dataset. As part of your analysis, you need to create a custom Python function that calculates a complex metric based on multiple columns in a Snowflake table. You want to apply this function to each row of the table and store the results in a new column. Which of the following approaches is the MOST efficient and scalable way to achieve this using Snowflake and Python?
Options
- ALoad the entire Snowflake table into a Pandas DataFrame, apply the Python function to each row
- BUse the '%%osql' magic command to execute a series of SQL UPDATE' statements that call the
- CCreate a Snowflake Python User-Defined Function (UDF) that encapsulates the calculation logic
- DIterate over the rows of the Snowflake table using the Snowflake Connector for Python, call the
- ECreate a stored procedure in Snowflake that runs the logic in a separate environment.
How the community answered
(15 responses)- A7% (1)
- C67% (10)
- D7% (1)
- E20% (3)
Explanation
Creating a Snowflake Python UDF (option C) is correct because UDFs execute server-side within Snowflake's distributed compute engine, meaning the function runs in parallel across all rows without moving data out of Snowflake - this is both efficient and scales automatically with data size.
Why the distractors fail:
- A (Pandas DataFrame): Loading the entire table into Pandas pulls all data across the network to the client, consuming memory and eliminating Snowflake's parallelism - this breaks down completely on large datasets.
- B (%%sql UPDATE statements): Row-by-row SQL UPDATEs are extremely slow due to per-statement overhead and lack of vectorization; this is an anti-pattern for bulk transformation.
- D (Snowflake Connector iteration): Iterating rows in Python via the connector is a client-side loop - O(n) round trips to Snowflake, catastrophically slow at scale.
- E (Stored procedure): Stored procedures can run logic but are designed for procedural control flow, not for applying a scalar function across every row efficiently; they don't inherently parallelize row-level computation the way a UDF does.
Memory tip: Think "UDF = push the function to the data." Whenever you need to apply custom logic per-row at scale, the answer is to register it as a UDF so Snowflake's engine runs it in parallel - never pull the data out to run Python on it.
Topics
Community Discussion
No community discussion yet for this question.