You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

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• B.
  
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• D.
  
• E.
  

You are using VS Code with the Snowflake extension to develop a Snowpark application. You have successfully connected to your Snowflake account and are writing a script that creates a stage and then loads data from a local file into a Snowflake table using Snowpark. However, you're encountering issues with file paths and permission errors. Which of the following strategies would best address these challenges and ensure your Snowpark application can reliably load data from local files?

• A. Leverage a network share and mount it as a drive in both your local development environment and the Snowflake environment. Then, use relative file paths in your Snowpark code.
• B. Use VS Code's remote development feature to run your Snowpark code directly on the Snowflake compute nodes. This will eliminate file path issues.
• C. Modify the Snowflake account-level parameters to allow direct access to the local file system. Use relative file paths to access the local file.
• D. Use absolute file paths in your Snowpark code when referring to local files. Ensure the Snowflake service account has read access to the local file system.
• E. Utilize Snowpark's 'session.file.put' to upload the local file to an internal or external stage. Then, use 'session.table.copy_into' to load data from the stage into the target table.

You are developing a Snowpark Python application that needs to process large datasets. You want to optimize performance by leveraging user-defined functions (UDFs) to perform complex calculations in parallel across the Snowflake data warehouse. Which of the following statements regarding Snowpark UDFs are TRUE?

• A. Snowpark UDFs automatically distribute the data and computation across multiple nodes in the Snowflake warehouse, but the distribution strategy cannot be controlled by the developer.
• B. Snowpark Python UDFs are always executed in a single process on the Snowflake warehouse, limiting their parallel processing capabilities.
• C. To ensure optimal performance, it is recommended to always use the default Snowflake Anaconda channel for UDF dependencies, as custom channels may introduce latency.
• D. Snowpark UDFs can be defined as either scalar UDFs (processing one row at a time) or vectorized UDFs (processing batches of rows), offering different performance characteristics.
• E. Snowpark UDFs can be defined using either Python or Java, providing flexibility in choosing the programming language best suited for the task. The Java UDF creation method will allow faster execution speeds.

You are setting up a VS Code development environment for Snowpark with the Snowflake extension. You want to ensure that you can securely authenticate to Snowflake and execute Snowpark code. Which of the following steps are essential to configure secure authentication within VS Code for Snowpark?

• A. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use MFA. Ensure the username and password is provided with a valid MFA token.
• B. Install the Snowflake VS Code extension and configure the connection settings to use OAuth. Ensure the OAuth client and secret are properly configured in Snowflake and referenced in the connection settings.
• C. Install the Snowflake VS Code extension and configure the Snowflake connection settings in the extension's configuration file using username and password.
• D. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Snowflake Native Authentication. Ensure that the user has the required permissions to authenticate using this method.
• E. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Key Pair authentication. Ensure the private key is securely stored and referenced in the connection settings.

You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

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• D.
  
• E.