SAP-C02 · Question #723
A company is running a web-crawling process on a list of target URLs to obtain training documents for machine learning training algorithms. A fleet of Amazon EC2 t2.micro instances pulls the target…
The correct answer is B. Convert the web-crawling process into an AWS Lambda function. Configure the Lambda function E. Modify the web-crawling process to store results in Amazon S3. To optimize costs for an infrequent web-crawling process that currently uses idle Amazon EC2 instances and EFS for .csv output, the architecture should be redesigned to leverage serverless computing and cost-effective object storage. Converting the web-crawling process into an…
Question
A company is running a web-crawling process on a list of target URLs to obtain training documents for machine learning training algorithms. A fleet of Amazon EC2 t2.micro instances pulls the target URLs from an Amazon Simple Queue Service (Amazon SQS) queue. The instances then write the result of the crawling algorithm as a .csv file to an Amazon Elastic File System (Amazon EFS) volume. The EFS volume is mounted on all instances of the fleet. A separate system adds the URLs to the SQS queue at infrequent rates. The instances crawl each URL in 10 seconds or less. Metrics indicate that some instances are idle when no URLs are in the SQS queue. A solutions architect needs to redesign the architecture to optimize costs. Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
Options
- AUse m5.8xlarge instances instead of t2.micro instances for the web-crawling process. Reduce the
- BConvert the web-crawling process into an AWS Lambda function. Configure the Lambda function
- CModify the web-crawling process to store results in Amazon Neptune.
- DModify the web-crawling process to store results in an Amazon Aurora Serverless MySQL
- EModify the web-crawling process to store results in Amazon S3.
How the community answered
(49 responses)- A12% (6)
- B59% (29)
- C6% (3)
- D22% (11)
Why each option
To optimize costs for an infrequent web-crawling process that currently uses idle Amazon EC2 instances and EFS for .csv output, the architecture should be redesigned to leverage serverless computing and cost-effective object storage. Converting the web-crawling process into an AWS Lambda function, triggered by the SQS queue, eliminates idle compute costs, and modifying the process to store results directly in Amazon S3 provides highly scalable and inexpensive storage for the .csv files.
Replacing t2.micro instances with larger m5.8xlarge instances would drastically increase compute costs, directly contradicting the goal of cost optimization.
Converting the web-crawling process into an AWS Lambda function is highly cost-effective for infrequent, event-driven workloads, as Lambda is a serverless compute service billed per invocation and duration, incurring no cost when idle. The 10-second crawl time is well within Lambda's execution limits, making it suitable for processing URLs from the SQS queue without idle EC2 instance costs.
Amazon Neptune is a graph database, which is not suitable or cost-effective for simply storing raw .csv web crawl results, introducing unnecessary complexity and cost.
Amazon Aurora Serverless MySQL is a relational database designed for structured data, which would be more complex and potentially more expensive than object storage for handling raw .csv files from a web crawl.
Storing web-crawling results as .csv files in Amazon S3 is a significantly more cost-effective and scalable solution than using Amazon EFS for simple object storage. S3 provides high durability and availability at a lower price point, especially for a large number of potentially small files.
Concept tested: Serverless architecture, cost optimization, compute scaling, storage selection.
Source: https://aws.amazon.com/lambda/pricing/
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