The optimal solution is Implement a scheduled scaling action that sets the desired EC2 capacity just before the nightly workload begins. Scheduled scaling allows you to proactively adjust the Auto Scaling groups desired capacity based on known usage patterns. Since the workload is predictable and always begins at the same time each day, this approach ensures the EC2 instances are already provisioned and ready when the batch job starts, eliminating the ramp-up delay and unnecessary waiting.

The option Adjust the Auto Scaling policy to add a larger number of instances during each scaling step might help reduce the ramp-up time, but it's still reactive. This means the Auto Scaling group waits for metrics like CPU utilization to exceed thresholds before it adds capacity. For predictable workloads, reactive scaling is less efficient than scheduling.

The choice Raise the maximum instance count in the Auto Scaling group to accommodate peak demand addresses a constraint only if the current max setting is too low. However, in this case, the issue isnt the maximum limitits the time it takes to scale up. Simply raising the cap doesnt ensure that the instances will launch earlier or faster.

The suggestion to Set a higher minimum capacity in the Auto Scaling group to ensure readiness for the batch jobs means keeping a larger number of instances running 24/7, which is not cost-effective. Since the workload is only heavy for a brief period each day, maintaining high capacity at all times would lead to unnecessary costs outside the batch window.

This question is important because it tests your understanding of scaling strategies for time-based workloads, which are common in batch processing and analytics. The trick to answering this type of question is to notice patterns like consistent start times and predictable capacity needsthese strongly indicate that scheduled scaling is the best fit.

The challenge lies in distinguishing between proactive and reactive scaling methods. Many options suggest scaling adjustments, but only one aligns with a known schedule and provides both performance and cost optimization.

Full AWS Practitioner Certification Question