You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

• A. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
• B. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
• C. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
• D. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
• E. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.

You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

• A. Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
• B. Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
• C. Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
• D. Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
• E. Restore the entire Snowflake account to a point in time before the incorrect transformation.

You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

• A. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
• B. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.
• C. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
• D. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
• E. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.

A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

• A. Clustering on followed by 'SALE_DATE'.
• B. Creating separate tables for each 'PRODUCT CATEGORY.
• C. Clustering only on PRODUCT_CATEGORY.
• D. Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
• E. Clustering only on 'SALE DATE