SOL-C01 · Question #311
A data scientist has developed a Streamlit application within a Snowflake Notebook to perform predictive analytics on customer churn. The application uses a pre-trained machine learning model stored a
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Question
A data scientist has developed a Streamlit application within a Snowflake Notebook to perform predictive analytics on customer churn. The application uses a pre-trained machine learning model stored as a Snowflake stage object. The model takes several customer features as input, which are stored in a Snowflake table called 'CUSTOMER FEATURES'. The data scientist needs to ensure that the model is loaded efficiently and that the inference is performed securely within the Snowflake environment, minimizing data movement. Which of the following approaches would be the MOST efficient and secure for loading the pre- trained model and performing the inference within the Snowflake environment using a Streamlit application?
Options
- ADownload the pre-trained model from the Snowflake stage to the Streamlit application's local
- BCreate a Snowflake external function (UDF) that loads the pre-trained model from the Snowflake
- CUse Streamlit's caching capabilities Cst.cache_resource') to load the pre-trained model from the
- DCreate a Snowflake stored procedure that loads the pre-trained model from the Snowflake stage
- EImplement Snowpark and load the model from the Snowflake stage, transforming and sending the
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