DOP-C02 Exam Dumps - Amazon Web Services AWS Certified Professional Questions and Answers

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer Documents Only:

To restrict direct access to API Gateway, add a custom header in the CloudFront distribution origin request and use a Lambda authorizer in API Gateway to validate that header. Only requests routed through CloudFront will include this header. This is the AWS-recommended pattern in “Secure your APIs behind CloudFront using custom headers and Lambda authorizers.”

Step 1: Reacting to RDS Storage Autoscaling Events Using Amazon EventBridgeAmazon RDS emits events when storage autoscaling occurs. To visualize these events in a CloudWatch dashboard, you can create an EventBridge rule that listens for these specific autoscaling events.

Action: Create an EventBridge rule that reacts to RDS storage autoscaling events from the RDS event stream.

Why: EventBridge allows you to listen to RDS events and route them to specific AWS services for processing.

Step 2: Creating a Custom CloudWatch Metric via LambdaOnce the EventBridge rule detects a storage autoscaling event, you can use a Lambda function to publish a custom metric to CloudWatch. This metric can then be visualized in a CloudWatch dashboard.

Action: Use a Lambda function to publish custom metrics to CloudWatch based on the RDS storage autoscaling events.

Why: Custom metrics allow you to track specific events like autoscaling and visualize them easily on a CloudWatch dashboard.

Activate Amazon Macie on the Production S3 Bucket:

Macie can identify and protect sensitive data such as PII.

Create a Step Functions state machine to automate data discovery and redaction before copying it to the development environment.

Example Step Functions state machine:

{

"Comment": "Anonymize PII and copy data",

"StartAt": "MacieDiscoveryJob",

"States": {

"MacieDiscoveryJob": {

"Type": "Task",

"Resource": "arn:aws:states:::macie:startClassificationJob",

"End": true

}

}

}

Create a Development Environment from CloudFormation Template:

Deploy the development environment in a new account using the existing CloudFormation template.

Schedule an EventBridge rule to start the Step Functions state machine on a weekly basis.

EventBridge rule example:

{

"ScheduleExpression": "rate(7 days)",

"StateMachineArn": "arn:aws:states:::stateMachine:AnonymizeAndCopyData"

}

By using Macie for data anonymization and Step Functions for automation, you ensure PII is properly handled before data transfer between environments.

To achieve multi-Region high availability for a serverless API-based application, the architecture must be duplicated in the second Region and supported by a multi-Region data layer and appropriate DNS routing. Option A provides the required multi-Region data layer using DynamoDB global tables, which replicate data automatically and asynchronously between Regions. This allows reads and writes in either Region and prevents cross-Region latency bottlenecks.

Option C ensures that the compute components—API Gateway REST API and AWS Lambda functions—are deployed in both Regions. Each Region operates independently, providing true active-active redundancy and allowing Route 53 to route traffic directly to either Region without dependency on the other.

Option E selects Route 53 latency-based routing, which directs each user to the Region with the lowest round-trip latency. This meets the requirement to route traffic based on the Region providing the best response time rather than only providing active-passive failover.

Option D (failover routing) supports active-standby topologies, not active-active. Option B creates a global secondary index, which does not provide multi-Region replication. Option F (multivalue routing) distributes traffic randomly and does not account for latency or Region placement.

Thus, combining DynamoDB global tables, duplicated serverless architecture, and latency-based routing fully satisfies the requirements.

The correct answer is C. Allowing users to deploy CloudFormation stacks using AWS Service Catalog only and enforcing the use of a launch constraint is the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. AWS Service Catalog is a service that enables organizations to create and manage catalogs of IT services that are approved for use on AWS. A launch constraint is a rule that specifies the role that AWS Service Catalog assumes when launching a product. By using a launch constraint, the DevOps engineer can control the permissions that the users have when launching a product. Using AWS Config rules to detect when resources have drifted from their expected state is the best way to automate the monitoring of the resources. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS Config rules are custom or managed rules that AWS Config uses to evaluate whether your AWS resources comply with your desired configurations. By using AWS Config rules, the DevOps engineer can track the changes in the resources and identify any non-compliant resources.

Option A is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A CloudFormation service role is an IAM role that CloudFormation assumes to create, update, or delete the stack resources. By using a CloudFormation service role, the DevOps engineer can control the permissions that CloudFormation has when acting on the resources, but not the permissions that the users have when launching a stack. Therefore, option A does not prevent the users from launching resources that are not approved by the company. Using CloudFormation drift detection to detect when resources have drifted from their expected state is a valid way to monitor the resources, but it is not as automated and scalable as using AWS Config rules. CloudFormation drift detection is a feature that enables you to detect whether a stack’s actual configuration differs, or has drifted, from its expected configuration. To use this feature, the DevOps engineer would need to manually initiate a drift detection operation on the stack or the stack resources, and then view the drift status and details in the CloudFormation console or API.

Option B is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only, as explained in option A. Using AWS Config rules to detect when resources have drifted from their expected state is a valid way to monitor the resources, as explained in option C.

Option D is incorrect because enforcing the use of a template constraint is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A template constraint is a rule that defines the values or properties that users can specify when launching a product. By using a template constraint, the DevOps engineer can control the parameters that the users can provide when launching a product, but not the permissions that the users have when launching a product. Therefore, option D does not prevent the users from launching resources that are not approved by the company. Using Amazon EventBridge notifications to detect when resources have drifted from their expected state is a less reliable and consistent solution than using AWS Config rules. Amazon EventBridge is a service that enables you to connect your applications with data from a variety of sources. Amazon EventBridge can deliver a stream of real-time data from event sources, such as AWS services, and route that data to targets, such as AWS Lambda functions. However, to use this solution, the DevOps engineer would need to configure the event source, the event bus, the event rule, and the event target for each resource type that needs to be monitored, which is more complex and error-prone than using AWS Config rules.

The correct answer is D. The scenario requires a solution that can migrate an application from on premises to AWS, run on specific versions of Apache Tomcat, HAProxy, and Varnish Cache, tune the operating system-level parameters, automate the deployment of new application versions, and scale and replace faulty servers automatically. Option D meets all these requirements by using AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and Amazon EC2 Auto Scaling. AWS CodeCommit is a fully managed source control service that hosts Git repositories and works with Git-based tools. AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services, including Amazon EC2, AWS Fargate, AWS Lambda, and on-premises servers. AWS CodePipeline is a fully managed continuous delivery service that helps automate the release pipelines for fast and reliable application updates. Amazon EC2 Auto Scaling helps maintain application availability and allows scaling of Amazon EC2 capacity up or down automatically according to the defined conditions. By using these services together, the DevOps engineer can migrate the application code to AWS, configure and install the necessary software using the appspec.yml file, automate the deployment process using the pipeline, and scale and replace the servers using the Auto Scaling group.

Option A is incorrect because AWS Fargate is a serverless compute engine for containers that works with Amazon ECS and Amazon EKS. Fargate removes the need to provision and manage servers, but it also limits the ability to tune the operating system-level parameters, which is a requirement in the scenario. Moreover, Fargate does not support HAProxy and Varnish Cache as sidecar containers, which are needed to run the application properly.

Option B is incorrect because AWS Elastic Beanstalk is a fully managed service that automates the deployment and scaling of web applications and services using familiar servers such as Apache, Nginx, Passenger, and IIS. However, Elastic Beanstalk does not support HAProxy and Varnish Cache as part of the Tomcat solution stack, which are needed to run the application properly. Moreover, Elastic Beanstalk web server tier environments are designed to serve HTTP requests, not to process background tasks, which is the purpose of worker tier environments.

Option C is incorrect because AWS Elastic Beanstalk worker tier environments are designed to process background tasks using a daemon process that runs on each Amazon EC2 instance in the environment. Worker tier environments are not suitable for running web applications that serve HTTP requests, which is the case in the scenario. Moreover, Elastic Beanstalk does not support HAProxy and Varnish Cache as part of the Tomcat solution stack, which are needed to run the application properly.

AWS CodeCommit

AWS CodeDeploy

AWS CodePipeline

Amazon EC2 Auto Scaling

AWS Fargate

AWS Elastic Beanstalk

Step 1: Creating an Identity Provider in IAMYou first need to configure AWS to trust the external identity provider (IdP), which in this case supports OpenID Connect (OIDC). The IdP will handle the authentication, and AWS will handle the authorization based on the IdP's token.

Action: Create an IAM Identity Provider (IdP) in AWS using the existing provider's URL, audience, and signature. This step is essential for establishing trust between AWS and the external IdP.

Why: This allows AWS to accept tokens from your external IdP (auth.company.com) for authentication.

S3 cross-Region replication (CRR) automatically replicates data between buckets across different AWS Regions. To enable CRR, you need to add a replication configuration to your source bucket that specifies the destination bucket, the IAM role, and the encryption type (optional). You also need to grant permissions to the IAM role to perform replication actions on both the source and destination buckets. Additionally, you can choose the destination storage class and enable additional replication options such as S3 Replication Time Control (S3 RTC) or S3 Batch Replication. https://medium.com/cloud-techies/s3-same-region-replication-srr-and-cross-region-replication-crr-34d446806bab https://aws.amazon.com/getting-started/hands-on/replicate-data-using-amazon-s3-replication/ https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication.html

AWS CodePipeline supports multiple stages including source, build, test, and deploy. The most efficient way to integrate automated testing is by adding an AWS CodeBuild action to the pipeline that runs the tests using a buildspec.yml file. CodeBuild can be configured to fail the pipeline automatically if tests fail, ensuring that deployments do not proceed to the staging environment. This pattern is directly supported and documented in AWS CodePipeline + CodeBuild CI/CD architecture guidance.