A web API runs on an Auto Scaling group (ASG) behind an Application Load Balancer (ALB). During traffic spikes, users experience request timeouts even though CPU stays below 40%. After investigation, you find the ASG often has too few healthy targets to handle the current request rate. Which change will best improve responsiveness during spikes?

Keep the ASG scaling policy based on CPU utilization, but increase the ASG min capacity by 50%.

Raising the minimum capacity can reduce the odds of running out of healthy targets, but the scaling decision is still driven by CPU. In this scenario, CPU is explicitly <40% during the timeouts, so CPU-based scaling may not trigger additional capacity early enough for the request-rate increase. This also permanently increases baseline cost even when traffic is low.

Enable EC2 detailed monitoring for one-minute granularity and keep CPU scaling.

More frequent CPU measurements may make scaling react slightly faster, but it does not change the underlying signal. Since CPU remains below 40% during timeouts, the CPU metric still won’t reflect the bottleneck (insufficient healthy targets for the request rate), so scaling may not increase capacity when needed.

Switch to scaling based on the ASG network out bytes metric only, ignoring ALB response metrics.

Network out bytes can correlate with traffic, but it is not a direct measure of user-perceived performance. It can be noisy (for example, from background data transfer) and it may not capture saturation effects that show up as rising ALB latency or request queuing. ALB request/latency metrics provide a closer feedback loop to timeouts.

What does this SAA-C03 question test?

Read the scenario before looking for a memorised answer.

What is the correct answer to this question?

The correct answer is: Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime. — Because CPU stays low, the timeouts are most consistent with a capacity/healthy-target problem relative to the request rate (for example, requests are queuing at the ALB because there are not enough healthy targets). A target tracking policy that uses ALB metrics like RequestCountPerTarget or TargetResponseTime scales in response to load and latency experienced through the ALB. When those metrics rise during a spike, the ASG adds instances so the ALB can route requests to more healthy targets, reducing queueing and preventing timeouts. A can raise baseline capacity, but the ASG still scales based on CPU, which is not the bottleneck in the scenario. C improves measurement frequency but still relies on a metric that remains low (<40%), so it may not trigger scale-out during spikes. D relies on network-level signals that do not directly reflect ALB queuing/latency and can miss the performance symptoms that users see.

What should I do if I get this SAA-C03 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.