SPS-C01 Exam Dumps | You are using Snowpark to build a machine learning model. You need to use a specific version of scikit-learn

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Question 23/151

You are using Snowpark to build a machine learning model. You need to use a specific version of scikit-learn that is not available in the default Anaconda channel managed by Snowflake. Which of the following approaches is the MOST RECOMMENDED way to manage and deploy this specific version of scikit-learn for your Snowpark application?

A. Specify the required scikit-learn version in the 'requirements.txt' file used to create your Snowpark environment, using pip install directly from a public PyPl mirror.

B. Create a custom Anaconda environment using 'conda create' with the desired scikit-learn version. Upload the environment YAML file to a Snowflake stage and specify it in the 'session.createDataFrame' call.

C. Download the scikit-learn package as a wheel file, upload it to a Snowflake stage, and use 'session.add_import' to make it available to your Snowpark session.

D. Create a custom Anaconda environment using 'conda create' with the desired scikit-learn version. Upload the zipped environment to a Snowflake stage and set the SNOWPARK PYTHON USE CONDAenvironment variable.

E. Install the scikit-learn package directly on the Snowflake compute nodes using a shell script executed during session initialization.

Correct Answer: D

The most recommended approach is (D). Creating a custom Anaconda environment and deploying that provides better control, reproducibility, and isolation for dependencies. A is possible, but can cause conflicts depending on the version in the managed Anaconda. 'session.createDataFrame' doesn't take environment specifications (B). Individual wheel files (C) can work for simple scenarios but are less maintainable for complex projects. Installing packages directly on compute nodes (E) is not possible as there's no direct control over the underlying infrastructure.

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