AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question
An ecommerce company runs its web tier in an Auto Scaling group behind an Application Load Balancer. CPU utilization rises above 80% for two hours starting at 12:00 PM each weekday, causing slow pages. The team wants CPU to stay under 60% during the surge without over-provisioning at other times. Which solution meets these requirements?
Enable predictive scaling on the Auto Scaling group and keep the target tracking policy at 60 percent CPU utilization.
Purchase Standard Reserved Instances equal to the peak capacity and disable scale-in on the Auto Scaling group.
Add two scheduled actions to the Auto Scaling group: raise desired capacity a few minutes before noon on weekdays and reduce capacity shortly after the peak ends.
Lower the CPU utilization target in the existing target-tracking policy from 60 percent to 40 percent so instances launch sooner.
Scheduled actions let you scale proactively at known times. Creating one action that raises the Auto Scaling group's desired (and optionally minimum) capacity a few minutes before the lunchtime spike, and a second action that lowers capacity after the spike, ensures instances are running when demand arrives and are terminated when they are no longer needed. This keeps CPU utilization under the 60 percent target and avoids paying for extra instances the rest of the day.
Predictive scaling uses machine-learning forecasts but needs historical data and may scale earlier or later than desired; it adds complexity without clear benefit when the spike occurs at a fixed time. Lowering the target in the existing policy still reacts after the spike begins, so pages will continue to slow. Buying Reserved Instances and disabling scale-in provides capacity but wastes money outside the two-hour window and does not adjust to future changes in load.
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AWS Certified CloudOps Engineer Associate SOA-C03
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