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Answer: Transition the web-crawling process to an AWS Lambda function, configured to fetch URLs from the SQS queue., Modify the web-crawling process to save results in Amazon S3.
Option B describes converting the web-crawling process into an AWS Lambda function, which is a highly cost-effective solution since Lambda charges are based on the number of requests and the execution time, eliminating the need to maintain idle EC2 instances. Option E suggests modifying the web-crawling process to store results in Amazon S3, which is a cost-effective and scalable storage solution compared to Amazon EFS for storing .csv files. Together, these changes will significantly optimize costs by leveraging serverless architecture and more cost-effective storage solutions.
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A company is utilizing a fleet of Amazon EC2 t2.micro instances to execute a web-crawling process aimed at gathering training documents for machine learning algorithms. These instances retrieve target URLs from an Amazon Simple Queue Service (Amazon SQS) queue and subsequently write the outcomes of the crawling operation into a .csv file stored on an Amazon Elastic File System (Amazon EFS) volume, which is shared across all instances in the fleet. The addition of URLs to the SQS queue is sporadic, and each URL is processed within a 10-second timeframe. Monitoring data reveals that certain instances remain inactive when the SQS queue is empty. To enhance cost efficiency, a solutions architect is tasked with revising the architecture. Which two actions will most effectively and economically address these requirements?
A
Upgrade the EC2 instances to m5.8xlarge types and decrease the fleet size by half.
B
Transition the web-crawling process to an AWS Lambda function, configured to fetch URLs from the SQS queue.
C
Alter the web-crawling process to save results in Amazon Neptune.
D
Adjust the web-crawling process to save results in an Amazon Aurora Serverless MySQL instance.
E
Modify the web-crawling process to save results in Amazon S3.