An ML engineer is configuring auto scaling for an inference component of a model that...

This issue occurs because target tracking auto scaling allows the endpoint to scale down to zero, and scaling up only happens after traffic arrives. At the start of the business day, no instances are running, so the first requests experience cold-start delays.

AWS documentation for Amazon SageMaker recommends using scheduled or step scaling policies when predictable traffic patterns exist. In this case, business hours are predictable, so the best practice is to proactively scale the endpoint before traffic arrives.

Option D correctly uses Amazon CloudWatch alarms with step scaling to increase the minimum instance count from zero to one at the start of the business day. This ensures at least one warm instance is ready to handle requests immediately, eliminating startup latency.

Options A, B, and C do not guarantee instance availability before traffic begins. Cooldown tuning and metric changes only react after load is detected.

Therefore, scheduled step scaling using CloudWatch alarms is the correct solution.