Amazon AWS DevOps Engineer Professional

1.

A company has developed a static website hosted on an Amazon S3 bucket. The website is deployed using AWS CloudFormation. The CloudFormation template defines an S3 bucket and a custom resource that copies content into the bucket from a source location. The company has decided that it needs to move the website to a new location, so the existing CloudFormation stack must be deleted and re-created. However, CloudFormation reports that the stack could not be deleted cleanly. What is the MOST likely cause and how can the DevOps Engineer mitigate this problem for this and future versions of the website?

Deletion has failed because the S3 bucket has an active website configuration. Modify the CloudFormation template to remove the Website Configuration properly 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.

2.

A company is creating a software solution that executes a specific parallel-processing mechanism. The software can scale to tens of servers in some special scenarios. This solution uses a proprietary library that is license-based, requiring that each individual server have a single, dedicated license installed. The company has 200 licenses and is planning to run 200 server nodes concurrently at most. The company has requested the following features:

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,
acquirean 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.Createan Auto Scaling litecycle 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 userdatascript 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 fromtheDynamoDB table. Monitor each instance and, in case of failure, replace the instance, then manually update the DynamoDB table.

3.

A publishing company used AWS Elastic Beanstalk, Amazon S3, and Amazon DynamoDB to develop a web application. The web application has increased dramatically in popularity, resulting in unpredictable spikes in traffic. A DevOps Engineer has noted that 90% of the requests are duplicate read requests. How can the Engineer improve the performance of the website?

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 Varnish 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.

4.

A Developer is maintaining a fleet of 50 Amazon EC2 Linux servers. The servers are part of an Amazon EC2 Auto Scaling group, and also use Elastic Load Balancing for load balancing.
Occasionally, some application servers are being terminated after failing ELB HTTP health checks. The Developer would like to perform a root cause analysis on the issue, but before being able to access application logs, the server is terminated. How can log collection be automated?

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
scriptto 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 AmazonS3,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 calledlogs,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 SSMRunCommand script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

5.

A DevOps Engineer is designing a deployment strategy for a web application. The application will use an Auto Scaling group to launch Amazon EC2 instances using an AMI. The same infrastructure will be deployed in multiple environments (development, test, and quality assurance). The deployment strategy should meet the following requirements:

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 environmentthe
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 readthetags 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 forallenvironments. 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 Lambdafunctionthat 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.

Amazon AWS DevOps Engineer Professional

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