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Answer: Deploy Amazon EC2 instances within an Auto Scaling group behind an Application Load Balancer for both the web and application tiers. Utilize Amazon Aurora PostgreSQL with Babelfish enabled to seamlessly replatform the SQL Server database.
Option A is the most cost-effective solution because it leverages AWS managed services without requiring a significant rewrite of the application, which aligns with the company's requirement. Deploying Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer ensures both frontend and application tiers are scalable. Utilizing Amazon Aurora PostgreSQL with Babelfish enables replatforming of the existing SQL Server database, which helps in reducing licensing costs and compatibility issues, as Babelfish allows PostgreSQL to understand T-SQL, the SQL Server dialect. This makes the transition smoother and more cost-effective.
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An online retail company is planning to migrate its legacy on-premises .NET application to AWS. This application consists of load-balanced frontend web servers, load-balanced application servers, and a Microsoft SQL Server database. The company aims to utilize AWS managed services as much as possible without requiring a complete rewrite of the application. A solutions architect is tasked with developing a solution that effectively addresses scaling issues and reduces licensing costs as the application scales. Which of the following solutions offers the most cost-effective approach to meet these requirements?
A
Deploy Amazon EC2 instances within an Auto Scaling group behind an Application Load Balancer for both the web and application tiers. Utilize Amazon Aurora PostgreSQL with Babelfish enabled to seamlessly replatform the SQL Server database.
B
Create server images using AWS Database Migration Service (AWS DMS), and deploy these images on Amazon EC2 instances. Configure these instances in an Auto Scaling group behind a Network Load Balancer for both the web and application tiers. Adopt Amazon DynamoDB as the database tier.
C
Containerize the web frontend and application tiers, and provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Set up an Auto Scaling group behind a Network Load Balancer for both tiers. Employ Amazon RDS for SQL Server to manage the database.
D
Refactor the application functions into AWS Lambda functions. Utilize Amazon API Gateway for both the web frontend and application tiers. Migrate the data to Amazon S3 and query it using Amazon Athena.