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A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?
Correct Answer: C
Creating a new Snowflake Anaconda environment is the most efficient and secure way to manage specific package versions. It leverages Snowflake's built-in dependency management capabilities and avoids external dependencies or manual package management within the application code. Options A and D introduce potential security risks and maintenance overhead. Option B is inefficient for dependency management. Option E will not work, as it tries to install dependencies at runtime, which isn't supported in Snowpark.