SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company runs a critical web application on Amazon EC2 instances behind an Application Load Balancer (ALB). The application experiences performance degradation during peak hours. CloudWatch metrics show that the CPU utilization of the EC2 instances regularly reaches 90-100% during these periods. The company wants to ensure the application remains responsive during peak loads while minimizing costs. Which solution should a solutions architect recommend?
⚠ Common exam trap
The trap here is assuming that scaling based on request count or a fixed schedule is always sufficient, when the actual bottleneck is CPU utilization, which requires a metric that reflects compute load.
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 a target tracking scaling policy on the Auto Scaling group with a predefined metric of ASGAverageCPUUtilization and a target value of 50%.
The application's performance degradation is directly linked to high CPU utilization on the EC2 instances. A target tracking scaling policy using the ASGAverageCPUUtilization metric with a target of 50% will automatically add or remove instances to keep CPU utilization near that level. This provides responsive scaling during peak loads and cost savings during low demand. Other options either do not directly address CPU load or require manual tuning and prediction.
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 a target tracking scaling policy on the Auto Scaling group with a predefined metric of ASGAverageCPUUtilization and a target value of 50%.
Why this is correct
Target tracking with ASGAverageCPUUtilization at 50% will automatically adjust the number of instances to keep average CPU utilization near the target. This directly addresses the CPU bottleneck by scaling out when utilization rises, ensuring responsiveness during peak loads. It is a simple and effective solution that also minimizes costs by scaling in when demand drops.
- ✗
Enable detailed monitoring for the EC2 instances and create a CloudWatch alarm that triggers a step scaling policy when CPU utilization exceeds 80%.
Why it's wrong here
Step scaling with a CloudWatch alarm can work, but it requires manual tuning of thresholds and scaling adjustments. It is more complex and less adaptive than target tracking, which automatically adjusts capacity to maintain a target metric. Detailed monitoring increases cost and does not inherently improve scaling responsiveness compared to a well-configured target tracking policy.
- ✗
Configure a scheduled scaling policy to increase the desired capacity during known peak hours.
Why it's wrong here
Scheduled scaling is effective only if peak hours are predictable and consistent. The scenario does not specify that peak loads occur at known times, so a scheduled policy could over-provision or under-provision. Additionally, it does not react to unexpected spikes, making it less reliable than dynamic scaling for ensuring responsiveness during variable loads.
- ✗
Configure a target tracking scaling policy on the Auto Scaling group with a predefined metric of ALBRequestCountPerTarget and a target value of 1000.
Why it's wrong here
While ALBRequestCountPerTarget can be a useful metric, it does not directly correlate with CPU utilization, which is the bottleneck here. A target value of 1000 may not be appropriate without historical data, and this policy might not effectively reduce CPU load because it scales based on request count rather than CPU usage. It is not the most direct solution to the CPU saturation issue.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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