A. Use Snowflake's event table to capture all exceptions, filtering for the UDF name. Redact potentially sensitive fields in the logs after extraction using a Python script executed on the provider side.

B. Implement structured logging within the Scala UDF using SLF4J and Logback. Include contextual information (e.g., transaction IDs, timestamps) at various stages of the UDF execution. Use Snowflake's masking policies on the event table to protect sensitive data during logging. This is the best method as it's the more granular and effective

C. Rely solely on Snowflake's query history to identify failing queries. Analyze the input data and execution plan to infer the cause of the NullPointerException.

D. Utilize 'System.out.println' statements within the Scala UDF to print debug messages directly to the query result. The consumer can view these messages to identify the source of the error, with the provider having no visibility.

E. Implement a custom error handling mechanism in Scala that catches 'NullPointerException', logs a generic error message with a unique error code to the event table, and then re-throws a more descriptive exception to the consumer. The consumer can then report the error code to the provider for further investigation. The provider can analyze the code, without actual consumer sensitive data.