Step 1: Reducing Replication Lag in Aurora Global DatabasesIn Amazon Aurora global databases, write operations on the primary cluster can be delayed due to the time it takes to replicate to secondary clusters, especially when there are multiple secondary regions involved.
Issue: The write operations are occasionally blocked due to the RPO setting, which guarantees replication within 60 seconds.
Action: Remove one of the secondary clusters from the global database.
Why: Fewer secondary clusters will reduce the overall replication lag, improving write performance and reducing the frequency of blocked writes.
Reference: AWS documentation on Aurora Global Database.
This corresponds to Option C: Remove one of the secondary clusters from the global database.

https://aws.amazon.com/blogs/mt/monitor-changes-and-auto-enable-logging-in-aws-cloudtrail/

S3 cross-Region replication (CRR) automatically replicates data between buckets across different AWS Regions. To enable CRR, you need to add a replication configuration to your source bucket that specifies the destination bucket, the IAM role, and the encryption type (optional). You also need to grant permissions to the IAM role to perform replication actions on both the source and destination buckets. Additionally, you can choose the destination storage class and enable additional replication options such as S3 Replication Time Control (S3 RTC) or S3 Batch Replication. https://medium.com/cloud-techies/s3-same-region-replication-srr-and-cross-region-replication-crr-34d446806bab https://aws.amazon.com/getting-started/hands-on/replicate-data-using-amazon-s3-replication/ https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication.html

The correct answer is D. The scenario requires a solution that can migrate an application from on premises to AWS, run on specific versions of Apache Tomcat, HAProxy, and Varnish Cache, tune the operating system- level parameters, automate the deployment of new application versions, and scale and replace faulty servers automatically. Option D meets all these requirements by using AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and Amazon EC2 Auto Scaling. AWS CodeCommit is a fully managed source control service that hosts Git repositories and works with Git-based tools. AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services, including Amazon EC2, AWS Fargate, AWS Lambda, and on-premises servers. AWS CodePipeline is a fully managed continuous delivery service that helps automate the release pipelines for fast and reliable application updates. Amazon EC2 Auto Scaling helps maintain application availability and allows scaling of Amazon EC2 capacity up or down automatically according to the defined conditions. By using these services together, the DevOps engineer can migrate the application code to AWS, configure and install the necessary software using the appspec.yml file, automate the deployment process using the pipeline, and scale and replace the servers using the Auto Scaling group.
Option A is incorrect because AWS Fargate is a serverless compute engine for containers that works with Amazon ECS and Amazon EKS. Fargate removes the need to provision and manage servers, but it also limits the ability to tune the operating system-level parameters, which is a requirement in the scenario. Moreover, Fargate does not support HAProxy and Varnish Cache as sidecar containers, which are needed to run the application properly.
Option B is incorrect because AWS Elastic Beanstalk is a fully managed service that automates the deployment and scaling of web applications and services using familiar servers such as Apache, Nginx, Passenger, and IIS. However, Elastic Beanstalk does not support HAProxy and Varnish Cache as part of the Tomcat solution stack, which are needed to run the application properly. Moreover, Elastic Beanstalk web server tier environments are designed to serve HTTP requests, not to process background tasks, which is the purpose of worker tier environments.
Option C is incorrect because AWS Elastic Beanstalk worker tier environments are designed to process background tasks using a daemon process that runs on each Amazon EC2 instance in the environment.
Worker tier environments are not suitable for running web applications that serve HTTP requests, which is the case in the scenario. Moreover, Elastic Beanstalk does not support HAProxy and Varnish Cache as part of the Tomcat solution stack, which are needed to run the application properly.
AWS CodeCommit
AWS CodeDeploy
AWS CodePipeline
Amazon EC2 Auto Scaling
AWS Fargate
AWS Elastic Beanstalk

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer Documents Only:
In CodePipeline's execution mode,
* PARALLEL mode for pull_request pipelines ensures that multiple feature branches can be tested simultaneously for quick feedback.
* QUEUED mode for main_branch ensures deployments run sequentially - each must finish before the next begins, preventing overlap.This configuration aligns with AWS CodePipeline best practices for trunk-based development and concurrent test pipelines.