SAP-C02 Design for New Solutions Practice Question
A company is designing a new application that will run on Amazon ECS with Fargate. The application consists of three microservices: Service A, Service B, and Service C. Service A receives HTTP requests from an Application Load Balancer and sends messages to an Amazon SQS queue. Service B polls the SQS queue and processes the messages, storing results in Amazon DynamoDB. Service C reads from DynamoDB and sends notifications via Amazon SNS. The company expects variable traffic and wants to minimize costs. During a load test, the team observes that Service B is not scaling fast enough, causing the SQS queue to grow. The team also notices that Service C is idle most of the time. Which solution should the company implement to improve scaling and reduce costs?
⚠ Common exam trap
Many exam-takers choose CPU-based scaling (Option B) because it is familiar, but they fail to recognize that queue depth is a more direct and responsive metric for scaling message-processing services, and they overlook the cost savings of replacing an idle Fargate service with a Lambda function triggered by DynamoDB Streams.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure Service B with a target tracking scaling policy based on the SQS queue backlog and convert Service C to an AWS Lambda function triggered by DynamoDB Streams.
It addresses both scaling and cost issues: Service B's scaling is improved by using a target tracking scaling policy based on the SQS queue backlog (ApproximateNumberOfMessagesVisible), which directly correlates to the work demand, ensuring faster and more precise scaling. Converting Service C to a Lambda function triggered by DynamoDB Streams eliminates idle compute costs from a constantly running Fargate service, as Lambda only runs when new data appears in DynamoDB, reducing costs significantly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Lambda with Provisioned Concurrency for Service B and keep Service C as a Fargate service.
Why it's wrong here
Provisioned Concurrency adds cost; Service C should be serverless to reduce idle cost.
- ✗
Use a step scaling policy for Service B based on CPU utilization and keep Service C as is.
Why it's wrong here
CPU utilization may not reflect queue backlog; Service C remains idle costing money.
- ✓
Configure Service B with a target tracking scaling policy based on the SQS queue backlog and convert Service C to an AWS Lambda function triggered by DynamoDB Streams.
Why this is correct
Target tracking scaling based on SQS backlog scales Service B appropriately; Lambda for Service C eliminates idle cost.
- ✗
Increase the number of ECS tasks for Service B manually and use a scheduled scaling policy for Service C.
Why it's wrong here
Manual scaling does not handle variable traffic; scheduled scaling is not suitable for idle service.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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