Amazon DOP-C02 Practice Test - Questions Answers, Page 25

* Step 1: Deploy the Automation Solution using CloudFormation StackSets To automate the process across multiple AWS accounts within an organization, you can use AWS CloudFormation StackSets. StackSets allow you to deploy CloudFormation templates to multiple accounts within an organization, ensuring consistent infrastructure and automation. Action: Use AWS CloudFormation StackSets to deploy the necessary resources across all AWS accounts. This includes deploying the Lambda function and security groups that will isolate compromised EC2 instances. Why: StackSets make it easy to deploy and manage resources across multiple AWS accounts, reducing the operational overhead.

This corresponds to Option A: Use AWS CloudFormation StackSets to deploy the CloudFormation stacks in all AWS accounts.

* Step 2: Isolate EC2 Instances using Lambda and Security Groups When an EC2 instance is compromised, it needs to be isolated from the network. This can be done by creating a security group with no inbound or outbound rules and attaching it to the instance. A Lambda function can handle this process and can be triggered automatically by an Amazon EventBridge rule when a specific tag (e.g., 'isolation') is applied to the compromised instance.

Action: Create a Lambda function that attaches an isolated security group (with no inbound or outbound rules) to the compromised EC2 instances. Set up an EventBridge rule to trigger the Lambda function when the 'isolation' tag is applied to the instance.

Why: This automates the isolation process, ensuring that any compromised instances are immediately cut off from the network, reducing the potential damage from the compromise.

This corresponds to Option E: Create an AWS CloudFormation template that creates an EC2 instance role that has no IAM policies attached. Configure the template to have a security group that has no inbound rules or outbound rules. Use the CloudFormation template to create an AWS Lambda function that attaches the IAM role to instances. Configure the Lambda function to replace any existing security groups with the new security group. Set up an Amazon EventBridge rule to invoke the Lambda function when a specific tag is applied to a compromised EC2 instance.

Step 1: Understanding Lambda Permissions and AWS Config The custom AWS Config rule evaluates resources and invokes an AWS Lambda function when a compliance check is triggered. For AWS Config to invoke the Lambda function, it requires permission to do so. Issue: The Lambda function fails to execute because AWS Config doesn't have permission to invoke it. Action: Modify the resource-based policy of the Lambda function to grant AWS Config permission to invoke the Lambda function. Why: Without this permission, AWS Config cannot trigger the Lambda function, which is why the evaluation fails.

This corresponds to Option A: Modify the Lambda function's resource policy to grant AWS Config permission to invoke the function.

Step 1: Using Service Control Policies (SCPs) for EC2 Security To limit the use of EC2 instance credentials to the specific EC2 instance they are assigned to, you can create a Service Control Policy (SCP) that verifies specific conditions, such as whether the EC2 instance's source VPC and private IP match expected values. Action: Create an SCP that checks whether the values of the aws:EC2InstanceSourceVPC and aws:SourceVpc condition keys are the same. Deny access if they are not. Why: This ensures that credentials cannot be used outside the designated EC2 instance or VPC.

Step 2: Further Validation with Private IPs The SCP should also verify that the EC2 instance's private IP matches the IP range specified for the VPC. If the instance's private IP does not match, access should be denied. Action: In the same SCP, check whether the values of the aws:EC2InstanceSourcePrivateIP and aws:VpcSourceIP condition keys are the same. Deny access if they are not. Why: This ensures that the credentials are only used within the specific EC2 instance and its associated VPC.

This corresponds to Option B: Create an SCP that checks whether the values of the aws:EC2InstanceSourceVPC and aws:SourceVpc condition keys are the same. Deny access if the values are not the same. In the same SCP check, check whether the values of the aws:EC2InstanceSourcePrivateIP and aws:VpcSourceIP condition keys are the same. Deny access if the values are not the same. Apply the SCP to the OU.

Step 1: Storing Docker Images in Amazon ECR Docker images can be large, and storing them in a centralized, scalable location can greatly reduce build times. Amazon Elastic Container Registry (ECR) is a fully managed container registry that stores, manages, and deploys Docker container images. Action: Store the Docker images in an ECR repository. Why: Storing Docker images in ECR ensures that Docker images can be reused across multiple builds, improving build performance by avoiding the need to rebuild the images from scratch.

Step 2: Implementing Docker Layer Caching in CodeBuild Docker layer caching is essential for improving performance in continuous integration pipelines. CodeBuild supports local caching of Docker layers, which speeds up builds that reuse Docker images across multiple runs.

Action: Implement Docker layer caching within the CodeBuild project.

Why: This improves performance by allowing frequently used Docker layers to be cached locally, avoiding the need to pull or build the layers every time.

This corresponds to Option A: Store the Docker images in an Amazon Elastic Container Registry (Amazon ECR) repository. Implement a local Docker layer cache for CodeBuild.

Step 1: Reducing Replication Lag in Aurora Global Databases In Amazon Aurora global databases, write operations on the primary cluster can be delayed due to the time it takes to replicate to secondary clusters, especially when there are multiple secondary regions involved. Issue: The write operations are occasionally blocked due to the RPO setting, which guarantees replication within 60 seconds. Action: Remove one of the secondary clusters from the global database. Why: Fewer secondary clusters will reduce the overall replication lag, improving write performance and reducing the frequency of blocked writes.

This corresponds to Option C: Remove one of the secondary clusters from the global database.

Step 1: Sending AWS WAF Logs to CloudWatch Logs AWS WAF allows you to log requests that are evaluated against your web ACLs. These logs can be sent directly to CloudWatch Logs, which enables real-time monitoring and analysis. Action: Configure the AWS WAF web ACL to send log messages to a CloudWatch Logs log group. Why: This allows the operations team to view the logs in real time and analyze patterns using CloudWatch metric filters.

Step 2: Creating CloudWatch Metric Filters CloudWatch metric filters can be used to search for specific patterns in log data. The operations team can create filters for certain log patterns and set up alarms based on these filters.

Action: Instruct the operations team to create CloudWatch metric filters to detect patterns in the WAF log output.

Why: Metric filters allow the team to trigger alarms based on specific patterns without needing to manually search through logs.

This corresponds to Option A: Create an Amazon CloudWatch Logs log group. Configure the appropriate AWS WAF web ACL to send log messages to the log group. Instruct the operations team to create CloudWatch metric filters.

Step 1: Handling Invalid Data with Failed Batch Items The Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline. Action: Keep the Lambda function's batch size the same and configure it to report failed batch items. Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

Step 2: Using an SQS Dead-Letter Queue (DLQ) Configuring a dead-letter queue (DLQ) for SQS will ensure that messages with invalid data, or those that cannot be processed successfully, are moved to the DLQ. This prevents such messages from clogging the queue and allows the system to focus on processing valid messages.

Action: Configure an SQS dead-letter queue for the main queue.

Why: A DLQ helps isolate problematic messages, preventing them from continuously reappearing in the queue and causing processing delays for valid messages.

Step 3: Maintaining the Lambda Function's Batch Size Keeping the current batch size allows the Lambda function to continue processing multiple messages at once. By addressing the failed items separately, there's no need to increase or reduce the batch size.

Action: Maintain the Lambda function's current batch size.

Why: Changing the batch size is unnecessary if the invalid messages are properly handled by reporting failed items and using a DLQ.

This corresponds to Option D: Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SQS dead-letter queue.

Step 1: Using AWS CloudFormation with ECS Blue/Green Deployments The requirement is to implement an ECS blue/green deployment where traffic is shifted gradually. AWS CodeDeploy supports such blue/green deployments with predefined configurations, like ECSLinear10PercentEvery1Minute, which shifts 10% of traffic every minute. Action: Use the AWS::CodeDeployBlueGreen transform and the appropriate hooks in the CloudFormation template. The ECSLinear10PercentEvery1Minute deployment configuration meets the requirement of shifting 10% of traffic every minute. Why: The transform and hook parameters in CloudFormation are essential for configuring the blue/green deployment with the desired traffic-shifting behavior.

This corresponds to Option B: Add the AWS::CodeDeployBlueGreen transform and the AWS::CodeDeploy::BlueGreen hook parameter with the CodeDeployDefault.ECSLinear10PercentEvery1Minutes deployment configuration.

* Step 1: Centralizing Image Scanning in a Shared Services Account The first requirement is to centralize the image scanning process, ensuring pre-scan and post-scan images are stored separately. This can be achieved by creating separate pre-scan and post-scan repositories in the shared services account, with the appropriate resource-based policies to control access. Action: Create separate ECR repositories for pre-scan and post-scan images in the shared services account. Configure resource-based policies to allow write access to pre-scan repositories and read access to post-scan repositories. Why: This ensures that images are isolated before and after the scan, following the compliance requirements.

This corresponds to Option A: Create Amazon Elastic Container Registry (Amazon ECR) repositories in the shared services account: one repository for each pre-scan image and one repository for each post-scan image. Configure Amazon ECR image scanning to run on new image pushes to the pre-scan repositories. Use resource-based policies to grant the organization write access to the pre-scan repositories and read access to the post-scan repositories.

* Step 2: Replication between Pre-Scan and Post-Scan Repositories To automate the transfer of images from the pre-scan repositories to the post-scan repositories (after they pass the security scan), you can configure image replication between the two repositories.

Action: Set up image replication between the pre-scan and post-scan repositories to move images that have passed the security scan.

Why: Replication ensures that only scanned and compliant images are available for deployment, streamlining the process with minimal administrative overhead.

This corresponds to Option C: Configure image replication for each image from the image's pre-scan repository to the image's post-scan repository.