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Continuous Improvement for Existing SolutionseasyMultiple ChoiceObjective-mapped

SAP-C02 Continuous Improvement for Existing Solutions Practice Question

A startup runs its application on Amazon ECS with Fargate launch type. The application uses an Application Load Balancer to distribute traffic. During a recent marketing campaign, the application experienced high latency and some requests returned 503 errors. The team suspects that the tasks are hitting resource limits. The team wants to automatically scale the tasks based on CPU utilization. Which solution should the team implement?

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 Application Auto Scaling for the ECS service with a target tracking scaling policy based on average CPU utilization.

Uses Application Auto Scaling with a target tracking scaling policy based on average CPU utilization, which automatically adjusts the desired count of ECS tasks to maintain CPU utilization. Option B stops idle tasks via Lambda, but does not scale based on CPU utilization. Option C creates an Auto Scaling group for the ECS cluster, which is designed for EC2 instances, not Fargate tasks. Option D uses Lambda to periodically check CPU and update desired count, which is inefficient compared to native Application Auto Scaling. Therefore, Option A is correct.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Configure Application Auto Scaling for the ECS service with a target tracking scaling policy based on average CPU utilization.

    Why this is correct

    Target tracking automatically adjusts the desired count to maintain a target CPU utilization.

  • Create a CloudWatch alarm that triggers a Lambda function to stop idle tasks.

    Why it's wrong here

    Stopping tasks does not address scaling up.

  • Create an Auto Scaling group for the ECS cluster and configure it to scale based on CPU utilization.

    Why it's wrong here

    Auto Scaling groups manage EC2 instances, not ECS tasks directly.

  • Use AWS Lambda to periodically check CPU utilization and update the desired count of the ECS service.

    Why it's wrong here

    Using Lambda to periodically poll CPU utilisation and manually adjust the desired count introduces a polling interval that cannot react quickly enough to sudden traffic spikes, whereas the correct solution—ECS Service Auto Scaling with a target tracking policy—adjusts capacity continuously based on real-time CloudWatch metrics. This approach is tempting because Lambda is often used for custom, event-driven scaling logic in systems that lack native auto-scaling support, such as when scaling third-party resources or performing scheduled capacity adjustments.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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