You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?

• A. Option A
• B. Option D
• C. Option C
• D. Option B
• E. Option E

You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

• A. Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
• B. Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
• C. Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.
• D. Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
• E. Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.

A data engineer is responsible for a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a data warehouse. The engineer needs to implement a notification system to alert them when specific data quality issues occur, such as data duplication exceeding a threshold or a sudden drop in data volume. Which approach offers the MOST flexible and scalable solution for implementing these notifications?

• A. Rely solely on Snowflake's Data Sharing feature to share the data with a data quality team who will manually review the data and report any issues.
• B. Develop custom SQL scripts to periodically query the data for quality issues and send email notifications using Snowflake's stored procedures and the 'EMAIL' external function.
• C. Implement a data quality monitoring tool that integrates with Snowflake via JDBC/ODBC and uses its own rules engine and notification system to detect and alert on data quality issues.
• D. Create a series of Snowflake Tasks that execute SQL queries to check for data quality issues. If an issue is detected, the task triggers an external function to send a notification to a messaging service (e.g., AWS SNS, Azure Event Grid).
• E. Use Snowflake's built-in resource monitors to track data volume and configure alerts based on predefined thresholds. This approach is simple but limited in its ability to detect complex data quality issues.

You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

• A. Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.
• B. Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.
• C. Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.
• D. Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
• E. Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.

You have a base table 'ORDERS' with columns 'ORDER ID, 'CUSTOMER D', 'ORDER DATE, and 'ORDER AMOUNT'. You need to create a view that aggregates the total order amount per customer per month. However, for data governance purposes, you need to ensure that the view only shows data for the last 3 months. What is the MOST efficient and secure way to create this view in Snowflake?

• A. Option A
• B. Option D
• C. Option C
• D. Option B
• E. Option E