You are tasked with optimizing a Snowpark application that performs complex geospatial calculations on a large dataset of location coordinates. The application is currently running on a standard Snowflake warehouse. Initial tests indicate that the application is CPU- bound. Which of the following actions would be MOST effective in improving the performance of this Snowpark application?

• A. Increasing the value of parameter at the account level.
• B. Partitioning the location data based on latitude and longitude and leveraging Snowpark's DataFrame API for filtering data before geospatial calculations.
• C. Switching to a larger Snowflake warehouse size (e.g., from X-Small to Small) with the same warehouse type.
• D. Switching to a Snowpark-optimized warehouse and increasing the warehouse size.
• E. Enable result caching and reduce the amount of data being processed

You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

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

You have written a Snowpark Python function that utilizes a UDF to perform complex string manipulation on a DataFrame containing customer reviews. When deploying this function using '@sproc.test_utils.mock_snowflake environment, the test fails with a 'ModuleNotFoundError' indicating that a custom Python library (e.g., is not available. You have already confirmed that the library is installed in your local development environment. What is the MOST reliable way to ensure the UDF has access to this dependency during local testing?

• A. Use before defining the UDF to explicitly include the library as a zipped file. Ensure the zipped file contains the directory.
• B. Manually copy the directory into the 'ltmp' directory on your local machine before running the test. Snowpark will automatically detect libraries in this directory.
• C. Install the globally on your local machine using 'pip install
• D. Append the path to the library to the 'PYTHONPATH' environment variable before executing the local tests. This will make the library available to the Snowpark session during testing.
• E. Include the directory within the same directory as your Snowpark Python function and explicitly import it using a relative path (e.g., 'from .text_processing_lib import ...4).

You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.

• A. Implement data filtering and aggregation as early as possible in the transformation pipeline to reduce the size of the DataFrame.
• B. Use smaller data types (e.g., ' Int16' instead of ' Int64') where appropriate to minimize memory footprint.
• C. Increase the warehouse size to provide more memory resources.
• D. Leverage the 'sample()' function to work with a smaller subset of the data for testing and debugging.
• E. Utilize the 'repartition()' or functions to control the number of partitions in the DataFrame and potentially reduce memory consumption per partition.