You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

• A. The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
• B. There is no code provided, so there is nothing wrong with it.
• C. The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.
• D. Without masking poliy code, it's impossible to determine if there is anything wrong.
• E. The WITH clause is unneccessary.

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.

A data engineering team has created a Snowflake Listing to share their company's sales data'. They want to allow consumers to access the Listing programmatically. The consumers need to know when new versions of the Listing are available. What is the MOST efficient method for consumers to be notified about new Listing versions without continuously polling Snowflake?

• A. Snowflake automatically notifies consumers via an event notification service (e.g., AWS SNS, Azure Event Grid) integrated with the Data Marketplace when a new version is available.
• B. The data consumer implements a custom webhook endpoint that the data provider calls whenever a new Listing version is published. The data provider will need to maintain a list of all consumers.
• C. The data provider manually sends email notifications to each consumer whenever a new version is created.
• D. The consumer sets up a scheduled task in Snowflake to periodically query the 'SHOW VERSIONS command for the Listing.

A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

• A. Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
• B. Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
• C. Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
• D. Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
• E. Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.