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Difficulty: HardServerless and Automated Scaling Architectures for Cost Efficiency

A logistics company is designing an automated route simulation platform. The platform receives route telemetry data files in an Amazon S3 bucket. A processing task must analyze each file to simulate traffic conditions and update a central tracking database. Each simulation must run sequentially in the exact chronological order the data is received to ensure state consistency. A single simulation run typically takes between 1212 to 2020 minutes to complete. The volume of incoming telemetry files is highly unpredictable, with periods of zero activity followed by sudden bursts of hundreds of uploads. The company needs to design a highly available, serverless, and cost-effective architecture.

Which TWO configurations should a solutions architect recommend to meet these requirements? (Select TWO.)

  1. Configure an Amazon SQS FIFO queue to capture Amazon S3 Event Notifications, ensuring that telemetry files are processed in the strict chronological order they were uploaded.Answer
  2. Deploy the simulation workloads as containerized tasks using Amazon ECS on AWS Fargate, and configure Auto Scaling based on queue depth metrics.Answer
  3. C
    Create an AWS Lambda function triggered directly by Amazon S3 Event Notifications to run the simulation logic and update the tracking database.
  4. D
    Configure a standard Amazon SQS queue to buffer incoming telemetry events, and use AWS Lambda functions to poll the queue and execute the simulation jobs.

Answer

The correct configurations are using an Amazon SQS FIFO queue to capture S3 events in chronological order, and executing the simulations as containerized tasks on Amazon ECS with AWS Fargate using scaling based on queue depth metrics.
The correct configurations combine Amazon SQS FIFO queues to maintain strict chronological message ordering and Amazon ECS on AWS Fargate to handle long-running simulation workloads. Since simulations take 1212 to 2020 minutes, they exceed the 1515-minute execution limit of AWS Lambda, requiring a containerized serverless solution that can scale down to zero when idle to optimize costs.

Step-by-Step Solution

1
Evaluate execution time requirements.
The simulation tasks run for 1212 to 2020 minutes. Since AWS Lambda functions have a maximum timeout limit of 1515 minutes, Lambda is not viable. AWS Fargate provides a serverless execution environment suitable for containerized long-running tasks.
Choosing a compute option that supports long-running execution without timing out is critical for successful completion.
2
Determine message ordering requirements.
The system must process simulations sequentially in the exact chronological order of file arrival. A standard Amazon SQS queue cannot guarantee this, making an Amazon SQS FIFO queue necessary to enforce ordering.
Strict chronological sequencing is required to prevent race conditions and preserve data consistency.
3
Implement scaling configuration for cost optimization.
To handle unpredictable traffic efficiently (scaling to zero when idle and scaling out during bursts), configure Auto Scaling for the Amazon ECS service using the queue depth metric from Amazon CloudWatch.
Basing scaling rules on SQS queue metrics ensures compute resources are provisioned only when workload backlog is present, minimizing idle costs.

Key Concept

Leveraging AWS Fargate for serverless workloads exceeding the AWS Lambda execution limit combined with SQS FIFO queues for strict ordering and cost-effective scaling.
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Serverless and Automated Scaling Architectures for Cost Efficiency