You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

• A. Option C
• B. Option E
• C. Option D
• D. Option A
• E. Option B

You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

• A. Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.
• B. Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
• C. Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
• D. Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
• E. Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.

You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?

• A. Option C
• B. Option E
• C. Option D
• D. Option A
• E. Option B

You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

• A. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
• B. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
• C. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
• D. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
• E. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.