SPS-C01 Exam Dumps | You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark.

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Question 75/151

You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark. These functions rely on several external Python packages and need to be versioned and managed effectively. Which of the following strategies provides the MOST robust and scalable solution for managing dependencies and deploying these functions in a reproducible manner?

A. Manually uploading the required Python packages to a Snowflake stage and specifying them in the 'packages' argument of the '@sf.UDF and 'session.udtf.register' calls. Update the packages on stage every time dependencies are upgraded.

B. Creating a 'requirements.txt' file, using 'conda' to create an environment.yml and use 'snowflake.snowpark.functions.udf and 'session.udtf.register' with the environment.yml. Update the environment.yml manually when dependencies are upgraded.

C. Creating a conda environment specification file (environment.yml) that lists all dependencies, storing the environment.yml file in a Snowflake stage. Update the environment.yml when dependencies are upgraded.

D. Creating a 'requirements.txt' file and including all required packages in it. Zipping this file and uploading it to a Snowflake stage, and specifying it in 'imports'.

E. Options A,B and C are all equally viable options

Correct Answer: B,C

Options B and C provide the most robust and scalable solution. Creating a conda environment specification file (environment.yml) allows for precise control over dependencies and their versions. Both allow other devs to work with the same environment. Uploading the yml allows to include it as a part of snowflake's udfs and udtfs. The difference is whether it can be done directly in snowpark (B) or through the CLI (C). Option A is less manageable as dependencies grow and is prone to manual errors. Option D is not the correct way to handle dependencies for UDFs/UDTFs; the 'imports' parameter is used for data files and other resources, not for installing Python packages.

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