You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

• A. SQL UDF with nested CASE statements for address standardization.
• B. JavaScript UDF utilizing regular expressions for simple string replacements.
• C. Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
• D. Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
• E. Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.

You're building a data product on the Snowflake Marketplace that includes a view that aggregates data from a table containing Personally Identifiable Information (PII). You need to ensure that consumers of your data product CANNOT directly access the underlying PII data but can only see the aggregated results from the view. What is the MOST secure and recommended approach to achieve this?

• A. Grant the 'SELECT privilege directly on the underlying PII table to the share used for the Marketplace listing, along with the 'SELECT privilege on 'sensitive data view'.
• B. Grant 'READ privilege on the internal stage containing the data files backing the PII table.
• C. Create a stored procedure that returns the aggregated data, and grant EXECUTE privilege on the stored procedure to the share. The stored procedure SELECTs from the PII table.
• D. Grant USAGE privilege on the database containing the PII table and to the share.
• E. Grant the 'SELECT privilege only on the to the share used for the Marketplace listing. Do not grant any privileges on the underlying PII table.

Consider the following Snowflake SQL API call to execute a stored procedure:

• A. Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.
• B. Set the parameter to and retrieve the result set directly from the API response.
• C. Use the parameter to specify which external functions are allowed to be called by the procedure.
• D. The stored procedure should handle the error handling for network disruptions and automatically retry.
• E. Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.

You have a large dataset stored in AWS S3 in Parquet format. The data is constantly updated by an external process, but you need to run read-only analytical queries against the most current data in Snowflake without ingesting it. Which approach is the MOST efficient and cost-effective way to achieve this, considering minimal latency for query results?

• A. Create a Snowflake external table pointing to the S3 location. Manually refresh the external table metadata periodically using the 'ALTER EXTERNAL TABLE REFRESH' command.
• B. Create a view on top of the S3 files using a custom Java UDF to read Parquet data directly. Then, query the view.
• C. Create a Snowflake internal stage, load the Parquet files into the stage periodically, and create a table from the staged files.
• D. Use Snowpipe to continuously ingest the Parquet files from S3 into a Snowflake table.
• E. Create an external table in Snowflake pointing to the S3 location. Configure automatic data refresh using the 'AUTO REFRESH' parameter and a notification integration.