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 requirement is not just to perform a one-time upgrade, but to ensure that Kubernetes and related components stay continuously within the AWS standard support window, with minimal ongoing effort. EKS Auto Mode is designed to reduce operational burden by managing cluster infrastructure, including control plane and data plane lifecycle, in a more automated fashion. When EKS Auto Mode is enabled and existing managed node groups are removed, EKS can manage compute capacity and associated upgrades in a more integrated, hands-off way, tracking supported versions automatically.
Option A (aws eks update-cluster-version) and Option C (eksctl upgrade) are both manual upgrade mechanisms. They can update clusters to a specific version but do not continuously ensure future upgrades as new supported versions are released; they require the DevOps team to schedule and execute each upgrade.
Option D (IaC refactor) is generally a good practice but does not inherently provide automatic version lifecycle management; it also introduces significant upfront effort compared to enabling Auto Mode.
Thus, enabling EKS Auto Mode and deprecating manual managed node groups provides the least operational overhead for staying within the EKS-supported version schedule.

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.
Reference: AWS documentation on Publishing Custom Metrics to CloudWatch.
This corresponds to Option A: Create an Amazon EventBridge rule that reacts to RDS storage autoscaling events from RDS events. Create an AWS Lambda function that publishes a CloudWatch custom metric.
Configure the EventBridge rule to invoke the Lambda function. Visualize the custom metric by using the CloudWatch dashboard.

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.
The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.
Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service Sending and Receiving Events Between AWS Accounts - Amazon EventBridge Working with Log Data - Amazon CloudWatch Logs Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs Embedded Metric Format - Amazon CloudWatch Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

Create an SQS dead-letter queue. Modify the existing queue by including a redrive policy that sets the Maximum Receives setting to 1 and sets the dead-letter queue ARN to the ARN of the newly created queue.
Instruct the scientists to use the dead-letter queue to review the data that is not valid. Reprocess this data at a later time.