You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

• A. The cloud storage event notifications are not properly configured to trigger Snowpipe.
• B. Snowflake does not support Avro format for Snowpipe.
• C. The file format definition in Snowflake is incompatible with the Avro schema.
• D. The Snowpipe is paused due to exceeding the daily quota.
• E. The data files in cloud storage are not being automatically detected by Snowpipe.

You are designing a data pipeline in Snowflake to process IoT sensor data'. The data arrives in JSON format, and you need to extract specific nested fields using a Snowpark UDF for performance reasons. Which of the following statements are true regarding best practices and limitations when working with complex JSON data and Snowpark UDFs (Python or Scala)? (Select all that apply)

• A. The maximum size of the JSON document that can be processed by a Snowpark UDF is directly limited by the maximum size of the UDF code itself (typically a few MB), requiring chunking strategies for large JSON payloads.
• B. When working with Snowpark Python UDFs, it's recommended to use the 'json' module in Python to parse the JSON data within the UDF, as it's optimized for Snowflake's internal JSON representation.
• C. Leverage Snowflake's built-in 'PARSE_JSON' function and 'GET_PATH' function outside of the UDF as much as possible before passing the data to the UDF to reduce the complexity within the UDF itself.
• D. Ensure the UDF is idempotent, meaning it produces the same output for the same input, as Snowflake might execute UDFs multiple times for optimization purposes.
• E. For highly complex JSON structures, consider using a Scala UDF with a robust JSON parsing library like Jackson or Gson for potentially better performance and control over error handling compared to Python UDFs.

A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

• A. Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
• B. Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
• C. Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.
• D. Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
• E. Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.

A data engineering team has deployed an external function that leverages a cloud-based machine learning model. They are experiencing intermittent errors and performance degradation, and they suspect issues with the external function's batching and error handling. Which of the following steps would BEST address these issues and improve the reliability and performance of the external function? (Select TWO)

• A. Remove the "RETURNS NULL ON NULL INPUT clause from the external function definition to force all inputs to be processed.
• B. Decrease the timeout period for the external function to quickly fail and retry in case of errors.
• C. Monitor the external function's execution metrics using Snowflake's query history and the remote service's monitoring tools to identify bottlenecks and errors.
• D. Implement robust error handling within the external function's code to catch exceptions and return meaningful error messages to Snowflake.
• E. Increase the 'MAX BATCH_ROWS' parameter of the external function to send larger batches of data to the remote service.

You are creating a Snowflake Listing to share data with multiple consumers. One consumer requires access to the complete dataset while other consumers need access to a subset of the data based on geographical region (e.g., only data related to the 'US'). You want to minimize data duplication and management overhead. Select all the valid ways to implement this using Snowflake Data Sharing features.

• A. Share the base tables in the Listing and use Row Access Policies to filter data based on region for specific consumers and allow full access to the entire dataset for the consumer requiring full access. Monitor usage through Snowflake's account usage views.
• B. Create multiple secure views, each filtered by region, and create a single Listing that shares all views. Grant access to the appropriate view based on the consumer's region.
• C. Share the base tables in the Listing and instruct each consumer to filter the data based on their region using a WHERE clause.
• D. Create multiple Listings, one for each region and one for the complete dataset.