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Zorluk: OrtaServerless and Automated Scaling Architectures for Cost Efficiency

A logistics company is designing a serverless telemetry tracking system for its fleet of 10,00010,000 delivery vehicles. Each vehicle uploads its GPS coordinates to AWS in unpredictable, sudden bursts throughout the day. The location updates must be processed in the exact order they are received for each vehicle to map route history accurately. The processing execution is lightweight, taking less than 200 ms200\text{ ms} per message. The processed coordinates must be stored in a NoSQL database. Which combination of AWS services and configurations will meet these requirements most cost-effectively? (Select TWO.)

  1. AWS Lambda to process the updates, triggered by an Amazon SQS FIFO queue.Cevap
  2. Amazon DynamoDB configured with on-demand capacity mode to store the telemetry data.Cevap
  3. C
    Amazon SQS standard queue to buffer incoming telemetry messages before sending them to AWS Lambda.
  4. D
    Amazon DynamoDB configured with provisioned capacity mode scaled to the maximum expected peak write throughput.
  5. E
    A continuously running AWS Lambda function that uses a long-running execution model to poll the telemetry endpoint.

Cevap

AWS Lambda triggered by an Amazon SQS FIFO queue, and Amazon DynamoDB configured with on-demand capacity mode.
The combination of AWS Lambda triggered by Amazon SQS FIFO queues and Amazon DynamoDB on-demand capacity mode provides a fully serverless, automatically scaling, and cost-efficient architecture. AWS Lambda scales dynamically to process bursty incoming messages without charging for idle time, while the SQS FIFO queue ensures coordinates are processed in strict order per vehicle. Amazon DynamoDB on-demand mode handles unpredictable spikes without requiring over-provisioning, ensuring costs directly align with usage.

Adım Adım Çözüm

1
Determine the optimal compute model based on workload characteristics.
Since the processing is lightweight (200 ms200\text{ ms}) and triggered in unpredictable bursts, AWS Lambda is chosen to scale down to zero when idle.
This avoids paying for idle compute resources that would occur with running servers continuously.
2
Select the correct messaging layer to enforce ordering.
An Amazon SQS FIFO queue is chosen over a standard queue.
FIFO queues guarantee that messages are processed in the exact order they arrive, fulfilling the route tracking requirement.
3
Select the optimal database and capacity model.
Amazon DynamoDB with on-demand capacity mode is selected.
On-demand mode accommodates sudden traffic bursts instantly without manual scaling configuration and incurs no cost when the system is idle.

Anahtar Kavram

Combining event-driven serverless compute (AWS Lambda) with order-preserving message queues (Amazon SQS FIFO) and auto-scaling databases (Amazon DynamoDB On-Demand) to optimize costs for unpredictable, sequential workloads.
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Serverless and Automated Scaling Architectures for Cost Efficiency