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.

6.

A large enterprise is deploying a web application on AWS. The application runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Auto Scaling group across multiple Availability Zones. The application stores data in an Amazon RDS Oracle DB instance and Amazon DynamoDB. There are separate environments for development, testing, and production. What is the MOST secure and flexible way to obtain password credentials during deployment?

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.

7.

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?

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
thedeployment 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 previouslydeployedrevision. 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.

8.

A DevOps Engineer must track the health of a stateless RESTful service sitting behind a Classic Load Balancer. The deployment of new application revisions is through a Cl/CD pipeline. If the service's latency increases beyond a defined threshold, deployment should be stopped until the service has recovered. Which of the following methods allow for the QUICKEST detection time?

Use Amazon CloudWatch metrics provided by Elastic Load Balancing to calculate average latency. Alarm and stop deployment when latency increases beyond the defined threshold. Use AWS Lambda and Elastic Load Balancing access logs to detect average latency. Alarm and stop deployment when latency increases beyond the defined threshold. Use AWS CodeDeploy's MinimumHealthyHosts setting to define thresholds for rolling back deployments. If these thresholds are breached, roll back the deployment. Use Metric Filters to parse application logs in Amazon CloudWatch Logs. Create a filter for latency. Alarm and stop deployment when latency increases beyond the defined threshold.

9.

A DevOps Engineer is building a continuous deployment pipeline for a serverless application using AWS CodePipeline and AWS CodeBuild. The source, build, and test stages have been created with the deploy stage remaining. The company wants to reduce the risk of an unsuccessful deployment by deploying to a specified subset of customers and monitoring prior to a full release to all customers. How should the deploy stage be configured to meet these
requirements?

Use AWS CloudFormation to publish a new version on every stack update. Then set up a CodePipeline approval action for a Developer to test and approve the new version. Finally, use a CodePipeline invoke action to update an
AWSLambda function to use the production alias Use CodeBuild to use the AWS CLI to update the AWS Lambda function code, then publish a new version of the function and update the production alias to point to the new version of the function. Use AWS CloudFormation to define the serverless application and AWS CodeDeploy to deploy the AWS Lambda functions using DeploymentPreference: Canary10Percentl5Minutes. Use AWS CloudFormation to publish a new version on every stack update. Use the RoutingConfig property of the AWS : :Lambda: : Alias resource to update the traffic routing during the stack update.

10.

An application has microservices spread across different AWS accounts and is integrated with an on-premises legacy system for some of its functionality. Because of the segmented architecture and missing logs, every time the application experiences issues, it is taking too long to gather the logs to identify the issues. A DevOps Engineer must fix the log aggregation process and provide a way to centrally analyze the logs. Which is the MOST efficient and cost-effective solution?

Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to export on-premises logs, and store the logs in an S3 bucket in a central account. Build an Amazon EMR cluster toreduce
the logs and derive the root cause. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to import on-premises logs. Store all logs in S3 buckets in individual accounts. Use Amazon Macie to write a query tosearchfor the required specific event-related data point. Collect system logs and application logs using the Amazon CloudWatch Logs agent. Install the CloudWatch Logs agent on the on-premises servers. Transfer all logs from AWS to the on-premises data center. Use anAmazonElasticsearch Logstash Kibana stack to analyze logs on premises. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Install a CloudWatch Logs agent for on-premises resources. Store all logs in an S3 bucket in a central account. Set up an Amazon S3 trigger andanAWS Lambda function to analyze incoming logs and automatically identify anomalies. Use Amazon Athena to run ad hoc queries on the logs in the central account.

Amazon AWS DevOps Engineer Professional

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