SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company runs a critical web application on a fleet of Amazon EC2 instances behind an Application Load Balancer (ALB). The operations team notices that during peak traffic, the ALB reports a high number of HTTP 5xx errors, and CloudWatch metrics show that the target group's HealthyHostCount drops significantly. The application logs indicate that the instances are running out of memory and becoming unresponsive. The company wants to improve the reliability of the application with minimal operational overhead. Which solution should a solutions architect recommend?
⚠ Common exam trap
The trap here is assuming that a larger instance type or scheduled scaling solves the problem, when the core issue is the lack of dynamic scaling based on the actual resource bottleneck.
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 an Auto Scaling group with a target tracking scaling policy based on memory utilization, using the CloudWatch agent to publish memory metrics.
The application becomes unresponsive due to memory exhaustion under peak load. A target tracking scaling policy based on memory utilization, fed by CloudWatch agent metrics, automatically adjusts capacity to maintain a target memory level. This directly addresses the root cause and is fully managed, requiring minimal operational effort. Other options either do not scale dynamically or do not react to memory pressure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the instance size of the EC2 instances in the Auto Scaling group to a larger memory-optimized instance type.
Why it's wrong here
Changing the instance type to a larger memory-optimized size provides more memory per instance but does not automatically scale the fleet in response to demand. During peak traffic, the same number of instances may still be overwhelmed, and the change requires manual intervention or a launch template update, adding operational effort without dynamic scaling.
- ✗
Create a scheduled scaling policy that increases the desired capacity of the Auto Scaling group during known peak hours.
Why it's wrong here
Scheduled scaling only works for predictable traffic patterns. If peak traffic occurs at unexpected times or varies in intensity, the group may still be under-provisioned, leading to continued 5xx errors. It does not react to actual memory utilization, so it cannot prevent unresponsiveness when memory spikes outside the schedule.
- ✓
Configure an Auto Scaling group with a target tracking scaling policy based on memory utilization, using the CloudWatch agent to publish memory metrics.
Why this is correct
Publishing memory metrics via the CloudWatch agent and using a target tracking policy on memory utilization allows the Auto Scaling group to add instances when memory pressure rises, directly addressing the root cause of unresponsiveness. This approach is fully managed, requires no custom logic, and scales automatically, meeting the minimal operational overhead requirement.
- ✗
Enable ELB health checks and set a shorter health check interval so that unhealthy instances are replaced faster.
Why it's wrong here
Faster health checks and instance replacement improve recovery after an instance becomes unhealthy, but they do not prevent the memory exhaustion that causes the unhealthiness. The application would still become unresponsive under load, and frequent replacements could cause flapping, increasing operational overhead without solving the underlying capacity 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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