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Answer: Develop a new AMI configured with the AWS Systems Manager Agent (SSM Agent). Use this new AMI to create a launch template for an Auto Scaling group. Employ smaller instances within the Auto Scaling group. Set up an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Configure the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon Aurora MySQL.
Option D is the correct answer because it provides a straightforward way to make the application highly available with minimal development effort. By creating a new AMI configured with the AWS Systems Manager Agent (SSM Agent) and using it to set up an Auto Scaling group with smaller instances, the company can ensure better resource utilization and automatic scaling based on CPU utilization. Additionally, using an Application Load Balancer will help distribute traffic across the instances, further enhancing availability. Migrating the database to Amazon Aurora MySQL is a natural choice as it is compatible with the current MySQL database, requiring minimal changes. This option avoids the complexities and potential risks associated with changing database types or re-architecting the application to work with containers or serverless functions.
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A company's solutions architect is tasked with evaluating an AWS workload that has been in operation for several years. The application tier is stateless and currently operates on a single, large Amazon EC2 instance launched from an AMI. The application's data is stored in a MySQL database hosted on another single EC2 instance. The application server frequently experiences 100% CPU utilization, leading to unresponsive behavior. Additionally, the company manually patches these instances, which has previously resulted in downtime. The company requires a solution that enhances the application's availability with minimal development effort. Which of the following solutions would best meet these requirements?
A
Transition the application tier to AWS Lambda functions within the existing VPC. Implement an Application Load Balancer to distribute traffic across the Lambda functions. Employ Amazon GuardDuty to scan the Lambda functions for security. Migrate the database to Amazon DocumentDB with MongoDB compatibility.
B
Modify the EC2 instance type to a smaller Graviton-powered instance. Utilize the existing AMI to create a launch template for an Auto Scaling group. Deploy an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Configure the Auto Scaling group to scale based on CPU utilization. Transition the database to Amazon DynamoDB.
C
Convert the application tier to run on Docker containers. Operate these containers on Amazon Elastic Container Service (ECS) with EC2 instances. Establish an Application Load Balancer to distribute traffic across the ECS cluster. Set up the ECS cluster to scale based on CPU utilization. Migrate the database to Amazon Neptune.
D
Develop a new AMI configured with the AWS Systems Manager Agent (SSM Agent). Use this new AMI to create a launch template for an Auto Scaling group. Employ smaller instances within the Auto Scaling group. Set up an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Configure the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon Aurora MySQL.