A solutions architect observes that a nightly batch job scales up automatically for 1 hour before the Auto Scaling group reaches the desired Amazon EC2 capacity. The peak capacity is the same every night and the batch jobs always start at 1 AM. The architect needs a cost-effective solution that allows the desired EC2 capacity to be reached quickly and lets the Auto Scaling group scale down after the jobs complete. What should the architect do?

The correct solution is to configure scheduled scaling. Since the batch job runs at a predictable time (1 AM) and requires a consistent peak capacity, scheduled scaling allows the Auto Scaling group to proactively scale up to the desired compute level before the job starts. This eliminates the 1-hour ramp-up time, ensuring resources are available immediately, and is cost-effective because the Auto Scaling group can scale down after the jobs complete. Increasing the minimum capacity would keep instances running unnecessarily throughout the day, increasing costs. Increasing the maximum capacity wouldn't address the slow ramp-up. Changing the scaling policy to add more instances per operation might speed up scaling but wouldn't guarantee the desired capacity is met before the job starts and could lead to over-provisioning if not carefully managed.