ARA-C01 Question 224
Single answerFunctionsA retail company stores customer profile data in a VARIANT column and uses a scalar Python UDF to normalize addresses during reporting. Analysts complain that reports are slow because the UDF is called for every row in large fact tables. The architecture team wants to reduce repeated computation while preserving consistent results across sessions and warehouses. Which approach should the architect recommend?
- A
Replace the Python UDF with a memoizable SQL UDF if the normalization logic can be expressed in SQL and depends only on its input arguments
- B
Keep the Python UDF, but mark it as VOLATILE so Snowflake can cache the output more aggressively
- C
Convert the scalar Python UDF to an aggregate UDF so Snowflake evaluates it once per query instead of once per row
- D
Wrap the Python UDF in a view and enable search optimization on the view so function results are persisted automatically
Show answer and explanation
Correct answer: A
Explanation
The key architectural issue is repeated execution of expensive scalar UDF logic on large datasets. In Snowflake, SQL UDFs are generally preferable when the logic can be expressed in SQL because they integrate more directly with the optimizer and avoid the overhead associated with external language runtimes. For deterministic logic with repeated input values, a memoizable SQL UDF can cache results based on arguments and reduce recomputation. Python UDFs do not use memoization in the same way, so simply keeping the Python implementation does not address the root cause. Aggregate UDFs are for set-based aggregation semantics and are not a substitute for scalar transformations. Search optimization improves certain lookup patterns but does not persist function outputs. Snowflake documentation on user-defined functions and memoizable SQL UDFs supports using deterministic SQL-based function implementations where possible for better performance and reuse.
- A. Correct.
Correct. Memoizable SQL UDFs are designed to cache and reuse results for deterministic SQL UDF evaluations based on input arguments, which can reduce repeated computation across calls. If the address normalization logic can be rewritten in SQL and is deterministic, this is the best fit for improving repeated lookups while preserving consistent results. This aligns with Snowflake best practices to prefer SQL UDFs when possible and use memoizable functions for repeated deterministic evaluations.
- B. Incorrect.
Incorrect. VOLATILE is not a mechanism to increase caching for Python UDFs. In Snowflake, memoization applies to eligible SQL UDFs, not Python UDFs. Choosing a volatility-related concept here reflects a common misconception that all function types support the same optimization and caching behavior.
- C. Incorrect.
Incorrect. Aggregate UDFs are intended for computations across sets of rows, not to avoid per-row execution of scalar transformation logic such as address normalization. Converting scalar row-by-row logic into an aggregate UDF would change the semantics of the computation and would not solve the problem described.
- D. Incorrect.
Incorrect. A view does not automatically persist or cache UDF outputs, and search optimization is intended to improve selective query access patterns, not to materialize function results. This option confuses search optimization and result persistence/materialization. If persistence were required, other design patterns such as materialized structures might be considered, but that is not what this option describes.