You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?
Correct Answer: B,E
Options B and E are the MOST appropriate for handling network errors. Using a 'try...except block (B) specifically targets the API call and allows for handling network-related exceptions gracefully. Logging the error to a Snowflake stage provides valuable debugging information. Retry with exponential backoff increases the chances of success during transient network issues. Option E improves upon option B with external and maintained libraries such as tenacity and returning a model output, not just a single value, when the error is recoverable or the data is missing. Option A, a global exception handler, is too broad and might mask other errors. Option C is a necessary prerequisite but does not address intermittent connectivity issues. Option D's 'ping' command is not reliable for determining API availability and might introduce unnecessary delays or false negatives. A complete end-to-end, complete solution must focus on addressing all aspects of code and execution.