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. 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.
• B. Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
• C. 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.
• D. 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.
• 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.

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. 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.
• B. 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.
• C. Create multiple Listings, one for each region and one for the complete dataset.
• D. Share the base tables in the Listing and instruct each consumer to filter the data based on their region using a WHERE clause.

You are tasked with designing a solution to load semi-structured data (JSON) from an AWS S3 bucket into a Snowflake table using Snowpipe and the REST API. The data in S3 is constantly being updated, and you need to ensure that only new or modified files are loaded into Snowflake. Which of the following steps are essential for implementing an efficient and cost-effective solution?

• A. Configure Snowpipe to automatically detect new files in the S3 bucket using event notifications, but manually refresh the pipe using SYSTEM $PIPE STATUS periodically to ensure that all files are processed.
• B. Create a Snowflake external function that polls the S3 bucket every minute, checks for new files using the LIST command, and then calls the Snowpipe REST API endpoint for each new file.
• C. Use the 'VALIDATION MODES copy option with 'RETURN_ALL RESULTS = TRUE to validate all data being loaded into the Snowflake table.
• D. Configure an S3 event notification to trigger a REST API call to the Snowpipe endpoint whenever a new or modified file is added to the S3 bucket. The API call should include the file name in the request.
• E. Configure auto-ingest using SQS queue and SNOWPIPE object. No need to manually call the REST API endpoint for data loading.

Your company is implementing data governance policies in Snowflake and wants to automatically classify data to track Personally Identifiable Information (PII). You have defined a classification policy with a tag 'PII' and associated tag values like 'Email', 'CreditCard', and 'SSN'. You want to monitor the usage of PII data'. Which of the following approaches is the MOST efficient way to track access and modifications to columns tagged with the 'PII' tag and any of its tag values?

• A. Use Snowflake's native data governance capabilities in conjunction with Snowflake Horizon to actively monitor data quality and access patterns, specifically focusing on PII-tagged columns.
• B. Create a scheduled task to query the INFORMATION SCHEMCOLUMNS view and filter based on the TAG DATABASE, TAG SCHEMA, and TAG NAME columns to identify PII columns, then query the QUERY_HISTORY view and filter based on the identified column names.
• C. Implement a custom UDF that intercepts all queries. Inside the UDF, check if any of the accessed tables or columns are tagged with 'PII', and log those queries into a separate logging table for monitoring.
• D. Create a masking policy that redacts all PII data. Monitor access attempts via the ACCESS HISTORY view. Although it hinders data usability, this approach guarantees no PII data leakage and allows easy monitoring.
• E. Enable Snowflake's data governance features, including object tagging and data classification. Then, leverage the ACCESS HISTORY view, filtering based on POLICY _ TAGS. This will show all accesses to data tagged as PII.

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. The stored procedure should handle the error handling for network disruptions and automatically retry.
• D. Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.
• E. Use the parameter to specify which external functions are allowed to be called by the procedure.