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The correct approach to meet the requirements for separate environment pipelines and automatic deployment for test environments is to create two AWS CodePipeline configurations, one for each environment. The production pipeline should have a manual approval step to ensure that changes are reviewed before being deployed to production. A single AWS CodeCommit repository with separate branches for each environment allows for organized and efficient code management. Each CodePipeline retrieves the source code from the appropriate branch in the repository. The deployment step utilizes AWS CloudFormation to deploy the Lambda functions, ensuring that the infrastructure as code is maintained and version-controlled.

AWS Lambda with Amazon API Gateway: Using AWS Lambda with Amazon API Gateway

Tutorial on using Lambda with API Gateway: Tutorial: Using Lambda with API Gateway

AWS CodePipeline automatic deployment: Set Up a Continuous Deployment Pipeline Using AWS CodePipeline

Building a pipeline for test and production stacks: Walkthrough: Building a pipeline for test and production stacks

Update the status of the affected CodeArtifact package version to deleted:

Deleting the vulnerable package version prevents it from being available for download by any users or systems, ensuring that the compromised version is not consumed.

Update the CodeArtifact package origin control settings to allow direct publishing and to block upstream operations:

By allowing direct publishing, the security team can publish the patched version of the package directly to the CodeArtifact repository.

Blocking upstream operations prevents the repository from automatically fetching and serving the vulnerable package version from upstream public repositories.

By deleting the vulnerable version and configuring the origin control settings to allow direct publishing and block upstream operations, the company ensures that only the patched version is available and the vulnerable version cannot be downloaded.

To meet the new guideline for application deployment, the company can use a combination of AWS CodePipeline and AWS CloudTrail. A manual approval action in CodePipeline allows the security team to review and approve changes before they are deployed. This action can be configured to pause the pipeline until approval is granted, ensuring that no changes move to production without the necessary sign-off. Additionally, by creating an AWS CloudTrail trail, all actions taken within CodePipeline, including approvals, are recorded and delivered to an Amazon S3 bucket. This provides an audit trail that can be retained for compliance and review purposes.

AWS CodePipeline’s manual approval action provides a way to ensure that a member of the security team can review and approve changes before they are deployed1.

AWS CloudTrail integration with CodePipeline allows for the recording and retention of all pipeline actions, including approvals, which can be stored in Amazon S3 for record-keeping2.

Comprehensive and Detailed Explanation From Exact Extract:

To add an automated test in AWS CodePipeline that checks the HTTP response code of a deployed application, the most straightforward and operationally efficient method is to create an AWS Lambda function that makes an HTTP request to the application URL, checks the response code, and then reports success or failure back to CodePipeline.

The Lambda function can be invoked as an action in the pipeline stage.

If the response code is not 200 OK, the Lambda function can return a failure, causing the pipeline stage to fail and halt further execution.

Option A’s CloudWatch synthetic canary monitors are useful for ongoing monitoring but are not natively integrated as CodePipeline actions to control pipeline flow.

Option C incorrectly uses CodeDeploy action for testing, which is not designed for this purpose.

Option D introduces unnecessary complexity by deploying API Gateway and Device Farm, which is not required for a simple HTTP status check.Therefore, option B provides a clean, simple, and fully automated way to enforce HTTP response validation within the pipeline.

Reference from AWS Official Documentation:

AWS CodePipeline Custom Actions:"You can create Lambda invoke actions in your pipeline stages to run custom validation or testing functions."(AWS CodePipeline User Guide - Lambda Actions)

AWS Lambda Function for Health Checks:"Lambda functions can be used to perform health checks or API response validations and report results back to invoking services."(AWS Lambda Developer Guide)

Comprehensive and Detailed Explanation From Exact Extract:

Amazon ECR supportsenhanced scanningpowered by Amazon Inspector, which provides deeper security scanning for container images including OS and programming language package vulnerabilities.

Enabling enhanced scanning (Option B) allows detection of CRITICAL and HIGH vulnerabilities.

Amazon ECR emits scan completion events via EventBridge, which can trigger Lambda functions. The Lambda function can process the scan results fromAmazon Inspectorand programmatically approve or reject the image in the CI/CD pipeline (Option D).

Basic scanning (Option A) is limited and does not integrate with Inspector.

Options C and E describe functionalities not natively supported (ECR does not automatically submit Rejected status; Clair is not used in AWS ECR scanning).

This solution will meet the requirements because it will use CloudFormation nested stacks and stack sets to deploy the templates more efficiently and consistently across multiple regions. Nested stacks allow the company to separate out common components and reuse templates, while stack sets allow the company to create stacks in multiple accounts and regions with a single template. The company can also use Amazon SNS to send notifications to the data engineering team whenever a change is made to the templates or the stacks. Amazon SNS is a service that allows you to publish messages to subscribers, such as email addresses, phone numbers, or other AWS services. By using Amazon SNS, the company can ensure that the data engineering team is aware of all changes to the templates and can take appropriate actions if needed. What is Amazon SNS? - Amazon Simple Notification Service

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-recover.html

Step 1: Understanding the Deletion FailureThe most likely reason why the CloudFormation stack failed to delete is that the S3 bucket was not empty. AWS CloudFormation cannot delete an S3 bucket that contains objects, so if the website files are still in the bucket, the deletion will fail.

Issue: The S3 bucket is not empty during deletion, preventing the stack from being deleted.

Step 2: Modifying the Custom Resource to Handle DeletionTo mitigate this issue, you can modify the Lambda function associated with the custom resource to automatically empty the S3 bucket when the stack is being deleted. By adding logic to handle the RequestType: Delete event, the function can recursively delete all objects in the bucket before allowing the stack to be deleted.

Action: Modify the Lambda function to recursively delete the objects in the S3 bucket when RequestType is set to Delete.

Why: This ensures that the S3 bucket is empty before CloudFormation tries to delete it, preventing the stack deletion failure.

https://docs.aws.amazon.com/streams/latest/dev/monitoring-with-cloudwatch.html

GetRecords.IteratorAgeMilliseconds - The age of the last record in all GetRecords calls made against a Kinesis stream, measured over the specified time period. Age is the difference between the current time and when the last record of the GetRecords call was written to the stream. The Minimum and Maximum statistics can be used to track the progress of Kinesis consumer applications. A value of zero indicates that the records being read are completely caught up.

AWS Secrets Manager is a secrets management service that helps you protect access to your applications, services, and IT resources. This service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Using Secrets Manager, you can secure and manage secrets used to access resources in the AWS Cloud, on third-party services, and on-premises. SSM parameter store and AWS Secret manager are both a secure option. However, Secrets manager is more flexible and has more options like password generation. Reference: https://www.1strategy.com/blog/2019/02/28/aws-parameter-store-vs-aws-secrets-manager/