Your application has an Auto Scaling group of three EC2 instances behind an Elastic Load Balancer. Your Auto Scalinggroup was updated with a new launch configuration that refers to an updated AMI. During the deployment, customers complained that they were receiving several errors even though all instances passed the ELB health checks. How can you prevent this from happening again?

• A.Createa new ELB and attach the Autoscaling Group to the ELB
• B.Createa new launch configuration with the updated AMI and associate it with the AutoScaling group.
  Increase the size of the group to six and when instances becomehealthy revert to three.
• C.Manuallyterminate the instances with the older launch configuration.
• D.Updatethe launch configuration instead of updating the Autoscaling Group

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Which Auto Scaling process would be helpful when testing new instances before sending traffic to them, while still keeping them in your Auto Scaling Group?

• A.Suspend the process AZ Rebalance
• B.Suspend the process Health Check
• C.Suspend the process Replace Unhealthy
• D.Suspend the process AddToLoadBalancer

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A new zero-day vulnerability was found in OpenSSL requiring the immediate patching of a production web fleet running on Amazon Linux. Currently, OS updates are performed manually on a monthly basis and deployed using updates to the production Auto Scaling Group's launch configuration. Which method should a DevOps Engineer use to update packages in-place without downtime?

• A.Define a new AWS OpsWorks layer to match the running production instances, and use a recipe to issue a package update command to all running production instances.
• B.Use AWS Inspector to run "yum upgrade" on all running production instances, and manually update the AMI for the next maintenance window.
• C.Use AWS CodePipline and AWS CodeBuild to generate new copies of these packages, and update the Auto Scaling group's launch configuration.
• D.Use Amazon EC2 Run Command to issue a package update command to all running production instances, and update the AMI for future deployments.

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A Development team is currently using AWS CodeDeploy to deploy an application revision to an Auto Scaling group. If the deployment process fails, it must be rolled back automatically and a notification must be sent.
What is the MOST effective configuration that can satisfy all of the requirements?

• A.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.
• B.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.
• C.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.
• D.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.

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A company is building a web and mobile application that uses a serverless architecture powered by AWS Lambda and Amazon API Gateway. The company wants to fully automate the backend Lambda deployment based on code that is pushed to the appropriate environment branch in an AWS CodeCommit repository. The deployment must have the following: *Separate environment pipelines for testing and production. *Automatic deployment that occurs for test environments only. Which steps should be taken to meet these requirements?

• A.Configure a new AWS CodePipeline service. Create a CodeCommit repository for each environment. Set up CodePipeline to retrieve the source code from the appropriate repository. Set up a deployment step to deploy the Lambda functions with AWS CloudFormation.
• B.Create two AWS CodePipeline configurations for test and production environments. Configure the production pipeline to have a manual approval step. Create a CodeCommit repository for each environment. Set up each CodePipeline to retrieve the source code from the appropriate repository. Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.
• C.Create an AWS CodeBuild configuration for test and production environments. Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment. Push the Lambda function code to an Amazon S3 bucket. Set up the deployment step to deploy the Lambda functions from the S3 bucket.
• D.Create two AWS CodePipeline configurations for test and production environments. Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment. Set up each CodePipeline to retrieve the source code from the appropriate branch in the repository. Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.

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