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Difficulty: MediumServerless and Automated Scaling Architectures for Cost Efficiency

A logistics company is designing an automated package tracking update ingestion system. Package scanning devices at various transit hubs upload status updates. These updates for each package must be processed sequentially to prevent out-of-order state updates. The volume of updates varies significantly, with massive surges during holiday shopping seasons and near-zero activity during early morning hours.

The company wants to design a serverless architecture that minimizes costs during idle periods and scales automatically to handle peak surges without administrative overhead.

Which TWO configurations should a solutions architect select to meet these requirements cost-effectively? (Select two.)

  1. Configure an Amazon DynamoDB table using on-demand capacity mode to store the package tracking states.Answer
  2. Route incoming tracking updates to an Amazon Simple Queue Service (Amazon SQS) FIFO queue, and configure an AWS Lambda function to process messages from the queue.Answer
  3. C
    Route incoming tracking updates to an Amazon Simple Queue Service (Amazon SQS) standard queue, and configure an AWS Lambda function to process messages from the queue.
  4. D
    Configure an Amazon DynamoDB table using provisioned capacity mode with Auto Scaling enabled to store the package tracking states.
  5. E
    Deploy a continuously running AWS Lambda function that loops indefinitely to poll the queue for tracking updates.

Answer

The correct choices are using Amazon DynamoDB in on-demand capacity mode and routing updates through an Amazon SQS FIFO queue processed by AWS Lambda.
The correct solution combined the use of Amazon SQS FIFO queues to guarantee package update order and AWS Lambda to process updates dynamically, alongside Amazon DynamoDB in on-demand capacity mode. This setup ensures that compute and database resources scale down to zero cost during idle periods, while auto-scaling to handle peak workloads with no administrative overhead.

Step-by-Step Solution

1
Analyze the database requirements for unpredictable spikes and idle periods.
Identify that DynamoDB on-demand capacity mode is the most cost-effective since it scales to zero during inactivity and handles immediate spikes.
To minimize idle database costs while ensuring scaling capability.
2
Analyze the processing queue requirements for strict sequential ordering.
Determine that an Amazon SQS FIFO queue is required to guarantee message ordering.
Standard queues do not guarantee first-in, first-out delivery, which is required to prevent out-of-order tracking history.
3
Select the compute model for processing queue messages.
Pair the SQS queue with an AWS Lambda function using event source mapping.
Event-driven Lambda executions scale automatically and cost nothing when the queue is empty, whereas continuous polling would lead to excessive compute costs.

Key Concept

Leveraging serverless architectures with SQS FIFO queues and DynamoDB on-demand capacity mode to achieve automatic scaling and cost optimization for spiky, order-sensitive workloads.
Estimated Time:1m 30s
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Topics

Serverless and Automated Scaling Architectures for Cost Efficiency