SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company runs a web application on EC2 instances in an Auto Scaling group. The application receives a variable workload. The company wants to scale based on a custom metric that tracks the number of active users. What is the MOST efficient way to achieve this?
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
✓
Create a target tracking scaling policy using the custom metric as the target.
A target tracking scaling policy automatically adjusts the number of instances to maintain a target value for the custom metric, which is efficient for variable workloads. Option A is incorrect because scheduled scaling assumes predictable patterns. Option B is incorrect because using Lambda adds complexity and overhead. Option C is incorrect because step scaling requires manual configuration of steps and is less efficient than target tracking.
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 a scheduled scaling policy to add or remove instances based on historical usage patterns.
Why it's wrong here
Scheduled scaling is for predictable patterns, not variable workloads.
- ✗
Use AWS Lambda to periodically evaluate the custom metric and adjust the desired capacity via API calls.
Why it's wrong here
This adds complexity and is less efficient than built-in scaling policies.
- ✗
Create a step scaling policy that uses CloudWatch alarms based on the custom metric.
Why it's wrong here
Step scaling requires defining steps and is less efficient than target tracking.
- ✓
Create a target tracking scaling policy using the custom metric as the target.
Why this is correct
Target tracking automatically adjusts capacity to maintain the target metric value.
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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