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AWS Cloud Architecture Quiz

Test your knowledge and skills in AWS cloud architecture with this engaging quiz! Designed for DevOps engineers and cloud enthusiasts, this quiz comprises 10 challenging questions that cover various aspects of AWS services and best practices.

Join now to ensure you understand:

Microservices and log aggregation
Automated build processes
Compliance and tagging strategies
Blue/Green deployment methods
Serverless architecture implementation

10 Questions2 MinutesCreated by DeployingCloud523

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 to reduce 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 to search for 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 an Amazon Elasticsearch 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 and an AWS 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.

A software company wants to automate the build process for a project where the code is stored in GitHub. When the repository is updated, source code should be compiled, tested, and pushed to Amazon S3.

Which combination of steps would address these requirements? (Select THREE.)

Add a buildspec.yml file to the source code with build instructions.

Configure a GitHub webhook to trigger a build every time a code change is pushed to the repository.

Create an AWS CodeBuild project with GitHub as the source repository.

Create an AWS CodeDeploy application with the Amazon EC2/On-Premises compute platform.

Create an AWS OpsWorks deployment with the install dependencies command.

Provision an Amazon EC2 instance to perform the build.

A company has established tagging and configuration standards for its infrastructure resources running on AWS. A DevOps Engineer is developing a design that will provide a near-real-time dashboard of the compliance posture with the ability to highlight violations.

Which approach meets the stated requirements?

Define the resource configurations in AWS Service Catalog, and monitor the AWS Service Catalog compliance and violations in Amazon CloudWatch. Then, set up and share a live CloudWatch dashboard. Set up Amazon SNS notifications for violations and corrections.

Use AWS Config to record configuration changes and output the data to an Amazon S3 bucket. Create an Amazon QuickSight analysis of the dataset, and use the information on dashboards and mobile devices.

Create a resource group that displays resources with the specified tags and those without tags. Use the AWS Management Console to view compliant and non-compliant resources.

Define the compliance and tagging requirements in Amazon inspector. Output the results to Amazon CloudWatch Logs. Build a metric filter to isolate the monitored elements of interest and present the data in a CloudWatch dashboard.

Your application is currently running on Amazon EC2 instances behind a load balancer. Your management has decided to use a Blue/Green deployment strategy. How should you implement this for each deployment?

Set up Amazon Route 53 health checks to fail over from any Amazon EC2 instance that is currently being deployed to.

Using AWS CloudFormation, create a test stack for validating the code, and then deploy the code to each production Amazon EC2 instance.

Create a new load balancer with new Amazon EC2 instances, carry out the deployment, and then switch DNS over to the new load balancer using Amazon Route 53 after testing.

Launch more Amazon EC2 instances to ensure high availability, de-register each Amazon EC2 instance from the load balancer, upgrade it, and test it, and then register it again with the load balancer.

A company is beginning to move to the AWS Cloud. Internal customers are classified into two groups according to their AWS skills: beginners and experts.

The DevOps Engineer needs to build a solution to allow beginners to deploy a restricted set of AWS architecture blueprints expresses as AWS CloudFormation templates. Deployment should only be possible on predetermined Virtual Private Clouds (VPCs). However, expert users should be able to deploy blueprints without constraints. Experts should also be able to access other AWS services, as needed.

How can the Engineer implement a solution to meet these requirements with the LEAST amount of overhead?

Apply constraints to the parameters in the templates, limiting the VPCs available for deployments. Store the templates on Amazon S3. Create an IAM group for beginners and give them access to the templates and CloudFormation. Create a separate group for experts, giving them access to the templates, CloudFormation, and other AWS services.

Store the templates on Amazon S3. Use AWS Service Catalog to create a portfolio of products based on those templates. Apply template constraints to the products with rules limiting VPCs available for deployments. Create an IAM group for beginners giving them access to the portfolio. Create a separate group for experts giving them access to the templates, CloudFormation, and other AWS services.

Store the templates on Amazon S3. Use AWS Service Catalog to create a portfolio of products based on those templates. Create an IAM role restricting VPCs available for creation of AWS resources. Apply a launch constraint to the products using this role. Create an IAM group for beginners giving them access to the portfolio. Create a separate group for experts giving them access to the portfolio and other AWS services.

Create two templates for each architecture blueprint where only one of them limits the VPC available for deployments. Store the templates in Amazon DynamoDB. Create an IAM group for beginners giving them access to the constrained templates and CloudFormation. Create a separate group for experts giving them access to the unconstrained templates, CloudFormation, and other AWS services.

Two teams are working together on different portions of an architecture and are using AWS CloudFormation to manage their resources. One team administers operating system-level updates and patches, while the other team manages application-level dependencies and updates. The Application team must take the most recent AMI when creating new instances and deploying the application.

What is the MOST scalable method for linking these two teams and processes?

The Operating System team uses CloudFormation to create new versions of their AMIs and lists the Amazon Resource names (ARNs) of the AMIs in an encrypted Amazon S3 object as part of the stack output section. The Application team uses a cross-stack reference to load the encrypted S3 object and obtain the most recent AMI ARNs.

The Operating System team uses CloudFormation stack to create an AWS CodePipeline pipeline that builds new AMIs, then places the latest AMI ARNs in an encrypted Amazon S3 object as part of the pipeline output. The Application team uses a cross-stack reference within their own CloudFormation template to get that S3 object location and obtain the most recent AMI ARNs to use when deploying their application.

The Operating System team uses CloudFormation stack to create an AWS CodePipeline pipeline that builds new AMIs. The team then places the AMI ARNs as parameters in AWS Systems Manager Parameter Store as part of the pipeline output. The Application team specifies a parameter of type ssm in their CloudFormation stack to obtain the most recent AMI ARN from the Parameter Store.

The Operating System team maintains a nested stack that includes both the operating system and Application team templates. The Operating System team uses a stack update to deploy updates to the application stack whenever the Application team changes the application code.

A company has developed a Node.js web application which provides REST services to store and retrieve time series data. The web application is built by the

Development team on company laptops, tested locally, and manually deployed to a single on-premises server, which accesses a local MySQL database. The company is starting a trial in two weeks, during which the application will undergo frequent updates based on customer feedback. The following requirements must be met:

*The team must be able to reliably build, test, and deploy new updates on a daily basis, without downtime or degraded performance.

*The application must be able to scale to meet an unpredictable number of concurrent users during the trial. Which action will allow the team to quickly meet these objectives?

Create two Amazon Lightsail virtual private servers for Node.js; one for test and one for production. Build the Node.js application using existing process and upload it to the new Lightsail test server using the AWS CLI. Test the application, and if it passes all tests, upload it to the production server. During the trial, monitor the production server usage, and if needed, increase performance by upgrading the instance type.

Develop an AWS CloudFormation template to create an Application Load Balancer and two Amazon EC2 instances with Amazon EBS (SSD) volumes in an Auto Scaling group with rolling updates enabled. Use AWS CodeBuild to build and test the Node.js application and store it in an Amazon S3 bucket. Use user- data scripts to install the application and the MySQL database on each EC2 instance. Update the stack to deploy new application versions.

Configure AWS Elastic Beanstalk to automatically build the application using AWS CodeBuild and to deploy it to a test environment that is configured to support auto scaling. Create a second Elastic Beanstalk environment for production. Use Amazon RDS to store data. When new versions of the applications have passed all tests, use Elastic Beanstalk " ̃swap cname' to promote the test environment to production.

Modify the application to use Amazon DynamoDB instead of a local MySQL database. Use AWS OpsWorks to create a stack for the application with a DynamoDB layer, an Application Load Balancer layer, and an Amazon EC2 instance layer. Use a Chef recipe to build the application and a Chef recipe to deploy the application to the EC2 instance layer. Use custom health checks to run unit tests on each instance with rollback on failure.

For auditing, analytics, and troubleshooting purposes, a DevOps Engineer for a data analytics application needs to collect all of the application and Linux system logs from the Amazon EC2 instances before termination. The company, on average, runs 10,000 instances in an Auto Scaling group. The company requires the ability to quickly find logs based on instance IDs and date ranges.

Which is the MOST cost-effective solution?

Create an EC2 Instance-terminate Lifecycle Action on the group, write a termination script for pushing logs into Amazon S3, and trigger an AWS Lambda function based on S3 PUT to create a catalog of log files in an Amazon DynamoDB table with the primary key being Instance ID and sort key being Instance Termination Date.

Create an EC2 Instance-terminate Lifecycle Action on the group, write a termination script for pushing logs into Amazon CloudWatch Logs, create a CloudWatch Events rule to trigger an AWS Lambda function to create a catalog of log files in an Amazon DynamoDB table with the primary key being Instance ID and sort key being Instance Termination Date.

Create an EC2 Instance-terminate Lifecycle Action on the group, create an Amazon CloudWatch Events rule based on it to trigger an AWS Lambda function for storing the logs in Amazon S3, and create a catalog of log files in an Amazon DynamoDB table with the primary key being Instance ID and sort key being Instance Termination Date.

Create an EC2 Instance-terminate Lifecycle Action on the group, push the logs into Amazon Kinesis Data Firehouse, and select Amazon ES as the destination for providing storage and search capability.

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 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.

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.

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.

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.

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.

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