CV0-004 Operations and Support Practice Question
A cloud operations team runs a containerized API on Amazon ECS with the Fargate launch type. During peak hours, CPU utilization on the tasks regularly reaches 95 percent and response latency doubles. The team wants the service to add tasks automatically before users notice degradation, and to remove them when demand drops. Which action should the team take?
⚠ Common exam trap
The trap here is assuming that giving each task more CPU, or enabling a cluster-wide performance setting, will scale capacity, when only a service scaling policy changes the number of running tasks.
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 Application Auto Scaling target tracking scaling policy on the ECS service using the ECSServiceAverageCPUUtilization metric with a target value and a scale-out cooldown.
Target tracking scaling on the ECS service is the native mechanism for elastic task capacity. It monitors the service-level average CPU metric and adjusts the desired count toward the configured target, scaling out ahead of user-visible degradation and scaling in afterward. Adjusting task size, cluster-level settings, or EC2 capacity providers does not change the number of running Fargate tasks in response to demand.
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 task definition CPU reservation from 1024 to 4096 CPU units and redeploy the service so each task can process more requests.
Why it's wrong here
Raising the task-level CPU reservation gives each individual task more compute but does not change how many tasks run. With a fixed desired count the same number of larger tasks still saturates, and the service will not automatically add capacity when load rises, so latency during peaks remains unaddressed.
- ✗
Create an EC2 Auto Scaling group with a launch configuration that runs the container image, and attach the group to the ECS cluster as a capacity provider.
Why it's wrong here
Capacity providers backed by EC2 Auto Scaling groups manage the underlying EC2 instances, not the number of Fargate tasks. With the Fargate launch type there are no EC2 instances to scale, so this approach cannot add or remove tasks and would not relieve the CPU saturation the service is experiencing.
- ✓
Configure an Application Auto Scaling target tracking scaling policy on the ECS service using the ECSServiceAverageCPUUtilization metric with a target value and a scale-out cooldown.
Why this is correct
Application Auto Scaling for ECS services supports target tracking policies that watch the ECSServiceAverageCPUUtilization metric and adjust the desired task count to hold utilization near the target. Because the metric reflects the whole service, Fargate tasks scale out before saturation and scale in when load falls, which directly addresses the latency spike.
- ✗
Enable burstable performance mode on the ECS cluster so tasks can consume additional CPU credits during peak hours.
Why it's wrong here
Burstable performance is an EC2 instance characteristic tied to T-family instances, not an ECS cluster setting, and Fargate tasks do not draw on CPU credits. Even if it applied, credits only allow short bursts on a single instance and would not add task capacity across the service to absorb sustained peak demand.
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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 CompTIA exam blueprint
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