Install the Amazon CloudWatch Logs logging agent on the ECS instances. Change the logging driver in the ECS task definition to 'awslogs'.

Download the Amazon CloudWatch Logs container instance from AWS and configure it as a task. Update the application service definitions to include the logging task.

Use Amazon CloudWatch Events to schedule an AWS Lambda function that will run every 60 seconds running the create-export -task CloudWatch Logs command, then point the output to the logging S3 bucket.

Enable access logging on the Application Load Balancer, then point it directly to the S3 logging bucket.

Enable access logging on the target groups that are used by the ECS services, then point it directly to the S3 logging bucket.

Create an Amazon Kinesis Data Firehose with a destination of the S3 logging bucket, then create an Amazon CloudWatch Logs subscription filter for Kinesis.

Create Amazon CloudWatch Events rules for CodeDeploy operations. Configure a CloudWatch Events rule to send out an Amazon SNS message when the deployment fails. Configure CodeDeploy to automatically roll back when the deployment fails.

Use available Amazon CloudWatch metrics for CodeDeploy to create CloudWatch alarms. Configure CloudWatch alarms to send out an Amazon SNS message when the deployment fails. Use AWS CLI to redeploy a previously deployed revision.

Configure a CodeDeploy agent to create a trigger that will send notification to Amazon SNS topics when the deployment fails. Configure CodeDeploy to automatically roll back when the deployment fails.

Use AWS CloudTrail to monitor API calls made by or on behalf of CodeDeploy in the AWS account. Send an Amazon SNS message when deployment fails. Use AWS CLI to redeploy a previously deployed revision.

Retrieve an access key from an AWS Systems Manager SecureString parameter to access AWS services. Retrieve the database credentials from a Systems Manager SecureString parameter.

Launch the EC2 instances with an EC2 IAM role to access AWS services. Retrieve the database credentials from AWS Secrets Manager.

Retrieve an access key from an AWS Systems Manager plaintext parameter to access AWS services. Retrieve the database credentials from a Systems Manager SecureString parameter.

Launch the EC2 instances with an EC2 IAM role to access AWS services. Store the database passwords in an encrypted config file with the application artifacts.

Preconfigure the AMI using an AWS Lambda function that launches an Amazon EC2 instance, and then runs a script to install the software and create the AMI. Configure an Auto Scaling lifecycle hook to determine which environment the instance is launched in, and, based on that finding, run a configuration script. Save the secrets on an .ini file and store them in Amazon S3. Retrieve the secrets using a configuration script in EC2 user data.

Preconfigure the AMI by installing all the software using AWS Systems Manager automation and configure Auto Scaling to tag the instances at launch with their specific environment. Then use a bootstrap script in user data to read the tags and configure settings for the environment. Use the AWS Systems Manager Parameter Store to store the secrets using AWS KMS.

Use a standard AMI from the AWS Marketplace. Configure Auto Scaling to detect the current environment. Install the software using a script in Amazon EC2 user data. Use AWS Secrets Manager to store the credentials for all environments.

Preconfigure the AMI by installing all the software and configuration for all environments. Configure Auto Scaling to tag the instances at launch with their environment. Use the Amazon EC2 user data to trigger an AWS Lambda function that reads the instance ID and then reconfigures the setting for the proper environment. Use the AWS Systems Manager Parameter Store to store the secrets using AWS KMS.

Use Auto Scaling lifecycle hooks to put instances in a Pending:Wait state. Create an Amazon CloudWatch Alarm for EC2 Instance Terminate Successful and trigger an AWS Lambda function that executes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create a Config rule for EC2 Instance-terminate Lifecycle Action and trigger a step function that executes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch subscription filter for EC2 Instance Terminate Successful and trigger a CloudWatch agent that executes a script to called logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch Events rule for EC2 Instance- terminate Lifecycle Action and trigger an AWS Lambda function that executes a SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

Use Amazon ElastiCache for Redis to cache repeated read requests to DynamoDB and AWS Elemental MediaStore to cache images stored in S3.

Use Amazon ElastiCache for Memcached to cache repeated read requests to DynamoDB and Amazon EFS to cache images stored in S3.

Use DynamoDB Accelerator to cache repeated read requests to DynamoDB and Amazon CloudFront to cache images stored in S3.

Use DynamoDB Streams to cache repeated read requests to DynamoDB and API Gateway to cache images stored in S3.

Upload the licenses to a private Amazon S3 bucket. Create an AWS CloudFormation template with a Mappings section for the licenses. In the template, create an Auto Scaling group to launch the servers. In the user data script, acquire an available license from the Mappings section. Create an Auto Scaling lifecycle hook, then use it to update the mapping after the instance is terminated.

Upload the licenses to an Amazon DynamoDB table. Create an AWS CloudFormation template that uses an Auto Scaling group to launch the servers. In the user data script, acquire an available license from the DynamoDB table. Create an Auto Scaling lifecycle hook, then use it to update the mapping after the instance is terminated.

Upload the licenses to a private Amazon S3 bucket. Populate an Amazon SQS queue with the list of licenses stored in S3. Create an AWS CloudFormation template that uses an Auto Scaling group to launch the servers. In the user data script acquire an available license from SQS. Create an Auto Scaling lifecycle hook, then use it to put the license back in SQS after the instance is terminated.

Upload the licenses to an Amazon DynamoDB table. Create an AWS CLI script to launch the servers by using the parameter --count, with min:max instances to launch. In the user data script, acquire an available license from the DynamoDB table. Monitor each instance and, in case of failure, replace the instance, then manually update the DynamoDB table.

Deletion has failed because the S3 bucket has an active website configuration. Modify the CloudFormation template to remove the WebsiteConfiguration property from the S3 bucket resource.

Deletion has failed because the S3 bucket is not empty. Modify the custom resource's AWS Lambda function code to recursively empty the bucket when RequestType is Delete.

Deletion has failed because the custom resource does not define a deletion policy. Add a DeletionPolicy property to the custom resource definition with a value of RemoveOnDeletion.

Deletion has failed because the S3 bucket is not empty. Modify the S3 bucket resource in the CloudFormation template to add a DeletionPolicy property with a value of Empty.

Deploy the backend application to multiple regions. Any update to the code repository triggers a two-stage build and deployment pipeline. A successful deployment in one region invokes an AWS Lambda function to copy the build artifacts to an Amazon S3 bucket in another region. After the artifact is copied, it triggers a deployment pipeline in the new region.

Deploy the backend application to multiple Availability Zones in a single region. Create an Amazon CloudFront distribution to serve the application backend to global customers. Any update to the code repository triggers a two-stage build-and-deployment pipeline. The pipeline deploys the backend application to all Availability Zones.

Deploy the backend application to multiple regions. Use AWS Direct Connect to serve the application backend to global customers. Any update to the code repository triggers a two-stage build-and-deployment pipeline in the region. After a successful deployment in the region, the pipeline continues to deploy the artifact to another region.

Deploy the backend application to multiple regions. Any update to the code repository triggers a two-stage build-and-deployment pipeline in the region. After a successful deployment in the region, the pipeline invokes the pipeline in another region and passes the build artifact location. The pipeline uses the artifact location and deploys applications in the new region.

Create an Application Load Balancer with two target groups. Set up the Application Load Balancer for Amazon API Gateway private integration. Associate one target group to the current version and the other target group to the new version. Configure API Gateway to route 10% of incoming traffic to the new version. As the new version becomes stable, configure API Gateway to send all traffic to the new version and detach the old version from the load balancer.

Create an alias for an AWS Lambda function pointing to both the current and new versions. Configure the alias to route 10% of incoming traffic to the new version. As the new version is considered stable, update the alias to route all traffic to the new version.

Create a failover record set in AWS Route 53 pointing to the AWS Lambda endpoints for the old and new versions. Configure Route 53 to route 10% of incoming traffic to the new version. As the new version becomes stable, update the DNS record to route all traffic to the new version.

Create an ELB Network Load Balancer with two target groups. Set up the Network Load Balancer for Amazon API Gateway private integration Associate one target group with the current version and the other target group with the new version. Configure the load balancer to route 10% of incoming traffic to the new version. As the new version becomes stable, detach the old version from the load balancer.

In Amazon API Gateway, create a canary release deployment by adding canary settings to the stage of a regular deployment. Configure API Gateway to route 10% of the incoming traffic to the canary release. As the canary release is considered stable, promote it to a production release