SAA-C03 Design High-Performing Architectures Practice Question
A financial services firm runs a stateless containerized trading dashboard on Amazon ECS with the Fargate launch type. The dashboard queries a backend over HTTPS and must present responses in under 200 ms. During market open, traffic triples within a few minutes and latency spikes because tasks take time to start. The team needs faster, more predictable scaling and wants to avoid over-provisioning during quiet periods. Which solution meets these requirements?
⚠ Common exam trap
The trap here is treating a longer cooldown as a stabilizing improvement when it actually delays the scale-out needed to protect latency.
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 ECS service using the ALBRequestCountPerTarget metric and set a short scale-out cooldown, keeping the minimum task count at a level that handles baseline traffic.
Application Auto Scaling target tracking on a request-count-per-target metric reacts to incoming demand rather than lagging CPU, which suits a burst at market open. Keeping scale-out cooldowns short and setting a modest minimum task count balances responsiveness with cost. The other approaches either delay scaling, add slower instance-based capacity, or rely on signals that fire too late.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the ECS service to the EC2 launch type with a large Auto Scaling group and enable cluster auto scaling.
Why it's wrong here
Moving to EC2 launch type replaces fast Fargate task launches with EC2 instance launches, which take longer to become available and add capacity management overhead. Cluster auto scaling scales instances, not tasks, so the dashboard still waits for task placement. This is slower and more complex than what the scenario requires.
- ✗
Configure a target tracking scaling policy on the ECS service using the ALBRequestCountPerTarget metric with a longer cooldown period.
Why it's wrong here
Target tracking on ALBRequestCountPerTarget is a reasonable scaling signal, but a longer cooldown delays scale-out actions, which directly worsens the latency spike during a rapid traffic surge. Cooldowns throttle responsiveness rather than improve it, so this setting works against the requirement for fast, predictable scaling at market open.
- ✓
Configure a target tracking scaling policy on the ECS service using the ALBRequestCountPerTarget metric and set a short scale-out cooldown, keeping the minimum task count at a level that handles baseline traffic.
Why this is correct
Target tracking with ALBRequestCountPerTarget scales on the actual demand signal seen by the load balancer, so tasks are added as requests rise rather than after CPU saturates. A short scale-out cooldown lets the service respond within minutes, and a sensible minimum task count covers baseline traffic while avoiding over-provisioning during quiet periods.
- ✗
Use AWS Application Auto Scaling with a scheduled scaling action and a target tracking policy on CPU utilization, plus a warm pool of pre-initialized tasks.
Why it's wrong here
Scheduled actions fit predictable peaks, but the scenario emphasizes unpredictable, fast surges, and a CPU-based target tracking policy reacts after utilization rises, which is slower than request-based signals. A warm pool concept does not exist as a native ECS feature, so this combination does not deliver the required responsiveness.
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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
This SAA-C03 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAA-C03 exam.