You're managing a Snowflake data warehouse and need to create a development environment for testing a complex stored procedure that updates a critical table, 'SALES DATA'. The procedure is located in the 'PRODUCTION' database and you want to ensure minimal impact to the production environment during development. You decide to use cloning and time travel. Which of the following strategies is the MOST efficient and safest approach to achieve this, minimizing downtime and resource consumption in production?

• A. Clone the "PRODUCTION' database. Immediately after cloning, use Time Travel to revert the 'SALES_DATR table in the development database to a state before the stored procedure was last run in production. Then clone the stored procedure itself. This gives a starting point without the procedure's impact.
• B. Clone the entire 'PRODUCTION' database into a new development database. This ensures developers have access to all necessary data and dependencies but consumes significant storage and may impact production performance during the cloning process.
• C. Create a snapshot of the 'SALES DATA' table using Time Travel at a specific timestamp (e.g., 1 hour ago), then clone only the stored procedure, updating it to point to the Time Travel version of 'SALES DATA' in the development environment. This provides a consistent dataset for testing while minimizing the impact on production and cloned data volumes.
• D. Clone only the 'SALES DATA' table into a development database. This minimizes storage consumption but requires developers to manually recreate or mock any dependencies the stored procedure has on other tables in the 'PRODUCTION' database.
• E. Clone the schema in which 'SALES_DATX is stored along with the stored procedure. Use time travel on the cloned schema to revert all objects in the schema to a point in time before the stored procedure was last run, then update the stored procedure to point to the cloned schema. This gives a consistent starting point for testing in development.

You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

• A. Increase the size of the Snowflake virtual warehouse to provide more processing power.
• B. Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
• C. Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
• D. Create a materialized view on top of the external table to pre-aggregate the data.
• E. Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.

You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?

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

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