AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question

An e-commerce site runs stateless web servers in two Availability Zones behind an Application Load Balancer. An Auto Scaling group uses a simple policy that adds two instances when average CPU exceeds 70%. During traffic spikes, new instances arrive late, causing 5xx errors. The team wants fewer errors without significant over-provisioning. Which change meets these goals with the least operational effort?

Enable predictive scaling for the Auto Scaling group.
Decrease the cooldown period and configure step scaling with larger instance increments.
Configure scheduled scaling to double the group's minimum capacity during business hours.
Replace the simple scaling policy with a target tracking policy that keeps Application Load Balancer RequestCountPerTarget at 100 requests.

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Answer Description

Target tracking policies automatically keep a specified metric near a chosen value and are recommended by AWS for most dynamic scaling scenarios. Using the predefined ALBRequestCountPerTarget metric lets the Auto Scaling group add or remove instances based on real-time load on the load balancer, reacting earlier than a CPU-based simple policy. Predictive scaling is useful for recurring patterns but offers less benefit for unpredictable spikes and requires additional configuration. Adjusting cooldowns and step scaling still depends on CPU thresholds and manual tuning, while scheduled scaling cannot respond to unexpected surges. Therefore, replacing the simple policy with a target tracking policy on request count best reduces errors without over-provisioning and involves minimal ongoing management.

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