Q10 — AWS SAA-C03 Ch.6

Question 10 of 65 | ← Chapter 6

Q375. A company has a web application that runs on premises. The application experiences latency issues during peak hours. The latency issues occur twice each month. At the start of a latency issue, the application's CPU utilization immediately increases to 10 times its normal amount. The company wants to migrate the application to AWS to improve latency. The company also wants to scale the application automatically when application demand increases. The company will use AWS Elastic Beanstalk for application deployment.Which solution will meet these requirements?

Correct Answer: A. Configure an Elastic Beanstalk environment to use burstable performance instances in unlimited mode.Configure the environment to scale based on requests

Explanation

This option the most suitable solution for the given requirements. By using burstable performance instances in unlimited mode, the application can handle sudden spikes in CPU utilization during peak hours. Burstable instances provide a baseline level CPU performance with the ability to burst above that baseline when needed. Configuring the environment to scale based on requests ensures that the application automatically scales up or down based on the incoming traffic, allowing it to handle increased demand and improve latency.Option B, configuring an Elastic Beanstalk environment to use compute optimized instances, does not address the issue of handling sudden spikes in CPU utilization. Compute optimized instances are designed for applications that require high-performance processors, but they may not be able to handle the increased load during peak hours.Option C, configuring the environment to scale on a schedule, does not address the dynamic nature the application demand. Scaling on a schedule would not be effective in handling the latency issues during peak hours.Option D, configuring the environment to scale on predictive metrics, may not be suitable as the latency issues occur twice each month without a predictable pattern. Predictive scaling relies on historical data and patterns to predict future demand, which may not be accurate in this scenario.