DEA-C02 Exam Dumps | You're designing a Snowpark data transformation pipeline that requires running a Python function on each

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Question 7/149

You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

A. Use 'DataFrame.foreach(lambda row: my_python_function(row))' to iterate through each row and apply the Python function.

B. Create a Snowpark UDF using input_types=[StringType()], return_type=StringType())' and apply it to the DataFrame using

C. Load the DataFrame into a Pandas DataFrame using and then apply the Python function using Pandas DataFrame operations.

D. Create a Snowpark UDTF using gudtf(output_schema=StructType([StructField('result', StringType())]), and apply it to the DataFrame using with a lateral flatten operation.

E. Define a stored procedure in Snowflake and use it to execute the Python code on each row by calling it in a loop.

Correct Answer: B,D

Options B and D are the best choices. UDFs and UDTFs allow you to leverage Snowflake's compute resources for parallel processing. The function execution happens on Snowflake's servers, close to the data, minimizing data transfer. By specifying 'packages=['my_package']' , you ensure that the external libraries are available in the execution environment. A UDF is suitable for one-to-one row transformations, while a UDTF is more appropriate if the Python function needs to return multiple rows for each input row (one-to-many). Option A, DataFrame.foreacW , is inefficient for large DataFrames as it processes rows sequentially. Option C, loading into Pandas, is also not ideal as it can lead to out-of-memory errors for very large DataFrames and transfers the data to the client machine. Option E, stored procedures with loops, is less scalable and efficient than UDFs or UDTFs.

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