A company hosts a public web application on AWS with a three-tier architecture: a frontend...

The problem is scale-out latency: when traffic spikes, new instances take time to launch, initialize, and pass health checks. This can temporarily degrade user experience even if Auto Scaling eventually adds enough capacity. The goal is to improve responsiveness to sudden spikes cost-effectively.

A key Auto Scaling feature for this situation is an EC2 Auto Scaling warm pool. A warm pool maintains a set of pre-initialized instances (stopped or running, depending on configuration) that Auto Scaling can rapidly transition into service. This reduces the time needed for instance launch and initialization, enabling faster scale-out during unexpected surges.

Option B is best because it supports the application’s peak needs by ensuring Auto Scaling can scale up to the required maximum capacity and keeps warm capacity ready. While a warm pool has some cost (especially if instances are kept running), it is typically more cost-effective than permanently running peak capacity all the time. It improves user experience by minimizing cold start delays during spikes.

Option A is counterproductive: lowering minimum capacity increases the likelihood of scale-out delays. Option C is illogical as written (“increase maximum to meet normal demand”); maximum should reflect peak potential, and a warm pool without sufficient max headroom will not solve the spike problem. Option D uses scheduled scaling, but the spike is unexpected, and increasing instance sizes increases cost and does not address launch delay; it may also reduce horizontal scaling flexibility.

Therefore, B is the most cost-effective way to improve scale-out performance and protect user experience during unexpected bursts.