https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-configurations.html

* Step 1: Attaching the CloudWatchAgentServerPolicy to the IAM Role
The CloudWatch agent needs permissions to collect and send metrics, including memory metrics, to Amazon CloudWatch. You can attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile or service account role to grant these permissions.
Action: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the EKS cluster uses.
Why: This ensures the CloudWatch agent has the necessary permissions to collect memory metrics.
Reference:
This corresponds to Option A: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the cluster uses.
* Step 2: Deploying the CloudWatch Agent to EC2 Instances
To collect memory metrics from the EC2 instances running in the EKS cluster, the CloudWatch agent needs to be deployed on these instances. The agent collects system-level metrics, including memory usage.
Action: Deploy the unified Amazon CloudWatch agent to the existing EC2 instances in the EKS cluster. Update the Amazon Machine Image (AMI) for future instances to include the CloudWatch agent.
Why: The CloudWatch agent allows you to collect detailed memory metrics from the EC2 instances, which is not enabled by default.
This corresponds to Option C: Collect performance metrics by deploying the unified Amazon CloudWatch agent to the existing EC2 instances in the cluster. Add the agent to the AMI for any new EC2 instances that are added to the cluster.
* Step 3: Analyzing Memory Metrics Using Container Insights
After collecting the memory metrics, you can analyze them using the pod_memory_utilization metric in Amazon CloudWatch Container Insights. This metric provides visibility into the memory usage of the containers (pods) in the EKS cluster.
Action: Analyze the pod_memory_utilization CloudWatch metric in the Container Insights namespace by using the Service dimension.
Why: This provides detailed insights into memory usage at the container level, which helps diagnose memory-related issues.
This corresponds to Option E: Analyze the pod_memory_utilization Amazon CloudWatch metric in the Container Insights namespace by using the Service dimension.

Verify No SCP Blocking Access:
Ensure that no Service Control Policy (SCP) is blocking access for developers to the S3 bucket. SCPs are applied at the organization or organizational unit (OU) level in AWS Organizations and can restrict what actions users and roles in the affected accounts can perform.
Verify No IAM Policy Permissions Boundaries Blocking Access:
IAM permissions boundaries can limit the maximum permissions that a user or role can have. Verify that these boundaries are not restricting access to the S3 bucket.
Make Necessary Changes to SCP and IAM Policy Permissions Boundaries:
Adjust the SCPs and IAM permissions boundaries if they are found to be the cause of the access issue. Make sure these changes are reflected in the code maintained in the AWS CodeCommit repository.
Invoke Deployment Through CloudFormation:
Commit the updated policies to the CodeCommit repository.
Use AWS CloudFormation to deploy the changes across the relevant accounts and resources to ensure that the updated permissions are applied consistently.
By ensuring no SCPs or IAM policy permissions boundaries are blocking access and making necessary changes if they are, the DevOps engineer can resolve the access issue for developers trying to access the S3 bucket.
Reference:
AWS SCPs
IAM Permissions Boundaries
Deploying CloudFormation Templates

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.
The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.
Reference:
1: Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service
2: Sending and Receiving Events Between AWS Accounts - Amazon EventBridge
3: Working with Log Data - Amazon CloudWatch Logs
4: Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs
5: Embedded Metric Format - Amazon CloudWatch
6: Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

The requirement is to create the infrastructure as code (IaC) and the CI/CD pipeline for the Lambda application that uses canary deployment and automated rollback. To do this, the DevOps team needs to use the following steps:
Create an AWS Serverless Application Model (AWS SAM) template for the application. AWS SAM is a framework that simplifies the development and deployment of serverless applications on AWS. AWS SAM allows customers to define Lambda functions and other resources in a template by using a simplified syntax. For each Lambda function, the DevOps team can include configurations for the AutoPublishAlias property and the DeploymentPreference property. The AutoPublishAlias property specifies the name of the alias that points to the latest version of the function. The DeploymentPreference property specifies how CodeDeploy deploys new versions of the function. By configuring the deployment configuration type to LambdaCanary10Percent10Minutes, the DevOps team can enable canary deployment with 10% of traffic shifted to the new version every 10 minutes.
Create an AWS CodeCommit repository. Create an AWS CodePipeline pipeline. Use the CodeCommit repository in a new source stage that starts the pipeline. Create an AWS CodeBuild project to deploy the AWS SAM template. CodeCommit is a fully managed source control service that hosts Git repositories. CodePipeline is a fully managed continuous delivery service that automates the release process of software applications. CodeBuild is a fully managed continuous integration service that compiles source code and runs tests. By using these services, the DevOps team can create a CI/CD pipeline for the Lambda application. The pipeline should use the CodeCommit repository as the source stage, where the DevOps team can upload the SAM template and source code. The pipeline should also use a CodeBuild project as the build stage, where the SAM template can be built and deployed.
Create an Amazon CloudWatch alarm for each Lambda function. Configure the alarms to enter the ALARM state if any errors are detected. Configure an evaluation period, dimensions for each Lambda function and version, and the namespace as AWS/Lambda on the Errors metric. CloudWatch is a service that monitors and collects metrics from AWS resources and applications. CloudWatch alarms are actions that are triggered when a metric crosses a specified threshold. By creating CloudWatch alarms for each Lambda function, the DevOps team can monitor the health and performance of each function version during deployment. By configuring the alarms to enter the ALARM state if any errors are detected, the DevOps team can enable automated rollback if any failures are reported.