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Worker Environments

5 minutes 5 Questions

Worker environments in AWS Elastic Beanstalk are designed to perform background tasks such as processing or analyzing large data sets, without impacting the application's frontend. They work alongside web server environments and allow you to offload computationally intensive or time-consuming opera…

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AWS Certified Solutions Architect - Worker Environments Example Questions

Test your knowledge of Worker Environments

Question 1

A company runs a multi-tier application with separate worker environments for background processing. Access to databases should be limited to the application layer. Which security best practice should they follow?

Correct Answer: Use security groups with proper ingress and egress rules

Using security groups with proper ingress and egress rules will help limit access to the databases by isolating the network interfaces for each tier. KMS encryption doesn't affect resource access, WAF secures web application attacks, VPC Flow Logs collects network traffic logs, Amazon Macie detects sensitive data at risk, and Inspector assesses application security vulnerabilities.

Question 2

A company's microservices communicate with each other through message queues. It is critical to minimize downtime and handle messages with strict priority. Which messaging service should the Solutions Architect configure for this use case?

Correct Answer: Amazon SQS

In this scenario, Amazon SQS (Simple Queue Service) is the best choice because it provides efficient and reliable message queuing between microservices while ensuring prioritization. SNS works for publish-subscribe patterns, Kinesis DataStreams is suitable for real-time data streams, Amazon MQ is effective for migrating from legacy messaging systems, Direct Connect facilit ates private connection, and AppFlow provides data transfer and integration.

Question 3

You must process millions of image files as batch jobs. Jobs are submitted to a queue, can run for up to 45 minutes, and require variable vCPU and memory per job. You want a fully managed service that schedules the jobs and automatically provisions and scales the compute environment based on the queued job volume. Which AWS service best fits this requirement?

Correct Answer: AWS Batch

AWS Batch is purpose-built for batch processing. It provides a managed job queue and scheduler, and it automatically provisions, scales, and deprovisions the underlying compute environments (EC2 or Fargate) based on the number and resource requirements of queued jobs. This makes it ideal for processing large volumes of image files where each job may run for many minutes and needs specific vCPU/memory. In contrast, AWS Lambda is constrained by runtime and memory limits and lacks a batch scheduler; Amazon EC2 Auto Scaling only scales instances without job scheduling; and Amazon ECS would require you to build and manage your own job queueing and scheduling logic.

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