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Requirement Summary:

Trigger anAWS Step Functions state machine(test execution)

Only when aspecific AWS CloudFormation stack is deployed

Option A: Create a Lambda function to invoke the state machine

✅Valid approach: Lambda can be used as anintermediary triggerfor Step Functions using the SDK (e.g., StartExecution API).

Offers flexibility (custom filtering, additional logic).

Option B: Create EventBridge rule filtering on UPDATE_IN_PROGRESS

❌Incorrect: UPDATE_IN_PROGRESS triggersbeforethe stack is fully deployed.

You need totrigger after deployment, such as UPDATE_COMPLETE or CREATE_COMPLETE.

Option C: EventBridge Pipes with Lambda target filtering on UPDATE_IN_PROGRESS

❌Incorrect for same reason as B (wrong timing).

Also, EventBridge Pipes are not necessary here if you're using rules directly.

Option D: Pipe with EventBridge Rule as source and Step Functions as target

❌Invalid setup: EventBridge Pipes useevent sources, not rules, as input.

This configuration is unsupported.

Option E: Add the state machine as a target of the EventBridge rule

✅Direct and low-overhead approach.

EventBridgenatively supports Step Functionsas a target.

You can trigger the state machinewithout a Lambdaif the filter matches (e.g., ResourceStatus = CREATE_COMPLETE, with the correct StackId).

Step Functions as EventBridge target:https://docs.aws.amazon.com/eventbridge/latest/userguide/eventbridge-target-step-functions.html

EventBridge CloudFormation events:https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-listing-event-history.html

StartExecution API:https://docs.aws.amazon.com/step-functions/latest/apireference/API_StartExecution.html

AWS Serverless Application Model (AWS SAM) is an open-source framework that enables developers to build and deploy serverless applications on AWS. AWS SAM uses a template specification that extends AWS CloudFormation to simplify the definition of serverless resources such as API Gateway, DynamoDB, and Lambda. The developer can use AWS SAM to define serverless resources in YAML and deploy them using the AWS SAM CLI.

Requirement Summary:

EKS containers sendlogs to CloudWatch Logs

Need toprocess logs in real time

Trigger logic based on aspecific errorin logs

Evaluate Options:

Option A: SNS topic with filter policy

❌SNS filter policies work onmessage attributes, not onCloudWatch Logs subscription filters

Option B: Subscription filter on log group

✅This enablesreal-time log processing

You can create asubscription filterwith apattern matching specific error strings

Sends matched logs to aLambda functionorKinesis

Option C: CloudWatch agent operator for trace collection

❌Irrelevant for log processing

Used formonitoring and tracing, notreal-time log filtering

Option D: Lambda function to process logs

✅Once logs match the pattern, Lambda canprocess and act(e.g., alert, store, analyze)

Option E: EventBridge rule on a schedule

❌Not real-time

Scheduled EventBridge rules are forcron-like tasks, not log stream processing

Subscription filters:https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

Real-time log processing with Lambda:https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#LambdaExample

Logs in EKS to CloudWatch:https://docs.aws.amazon.com/eks/latest/userguide/fargate-logging.html

API Gateway Stage Variables:These are designed for configuring dynamic values for your APIs in different deployment stages (dev, test, prod). Here's how to use them for third-party endpoints:

In the API Gateway console, access the "Stages" section of your API.

For each stage, create a stage variable named something like thirdPartyEndpoint.

Set the value of this variable to the actual endpoint URL for that specific environment.

When configuring API requests within your API Gateway method, reference this endpoint using ${stageVariables.thirdPartyEndpoint}.

Why Stage Variables Excel Here:

Environment Isolation: This approach keeps the endpoint configuration specific to each deployment stage, ensuring the right endpoints are used during development, testing, and production cycles.

Ease of Management: You manage the endpoints directly through the API Gateway console without additional infrastructure.

This solution meets the requirements in the most operationally efficient manner because it does not require any infrastructure provisioning or management. The developer can create a Lambda function that makes the API call and configure an EventBridge rule that triggers the function once a day at a designated time. This is a serverless solution that scales automatically and only charges for the execution time of the function.

This solution will meet the requirements by allowing the developer to control what data is sent to X-Ray and CloudWatch from the application code. The developer can filter out any PII from the trace messages before sending them to X-Ray and CloudWatch, ensuring that no PII goes outside of the EC2 instances. Option B is not optimal because it will automatically instrument all incoming and outgoing requests from the application, which may include PII in the trace messages. Option C is not optimal because it will require additional services and costs to use Amazon Macie and AWS Lambda, which may not be able to detect and hide all PII from the trace messages. Option D is not optimal because it will use Open Telemetry instead of X-Ray, which may not be compatible with CloudWatch and other AWS services.

Why Option C is Correct:Ensuring that pipeline artifacts are encrypted with a customer-managed AWS KMS key involves configuring the S3 bucket policy to require encryption. This policy ensures all objects uploaded to the bucket are encrypted with the specified KMS key.

Why Other Options are Incorrect:

Option A:A gateway endpoint improves S3 access efficiency but does not enforce encryption.

Option B:Encrypting CloudWatch Logs is unrelated to securing pipeline artifacts.

Option D:Configuring SNS for encryption does not affect the artifacts stored in the S3 bucket.

AWS Documentation References:

Using Server-Side Encryption with S3 Bucket Policies

This solution meets the requirements most cost-effectively because it optimizes the photos on demand and caches them for future requests. Lambda@Edge allows the developer to run Lambda functions at AWS locations closer to viewers, which can reduce latency and improve photo quality. The developer can create a Lambda@Edge function that uses the GET parameters from each request to optimize the photos with the required dimensions and resolutions and returns them as a response. The function can also store a copy of the processed photos on Amazon S3 for subsequent requests, which can reduce processing time and costs. Using S3 Batch Operations to create new variants of the photos will incur additional storage costs and may not cover all possible dimensions and resolutions. Creating a dynamic CloudFront origin or a Lambda@Edge function to route requests to corresponding photo variants will require maintaining a mapping of device types and photo variants, which can be complex and error-prone.