What should a solutions architect do to maintain the desired performance across all instances in the group?
Use a simple scaling policy to dynamically scale the Auto Scaling group.
Use a target tracking policy to dynamically scale the Auto Scaling group.
Use an AWS Lambda function ta update the desired Auto Scaling group capacity.
Use scheduled scaling actions to scale up and scale down the Auto Scaling group.
Explanations:
A simple scaling policy may not accurately maintain CPU utilization at 40%, as it often relies on specific thresholds rather than dynamic adjustments based on current metrics.
A target tracking policy adjusts the Auto Scaling group based on the specified target (in this case, CPU utilization at 40%), making it the best option for maintaining desired performance dynamically.
While AWS Lambda can manage scaling, it is not the best choice for maintaining consistent CPU utilization since it requires manual triggering and may not react quickly enough to changes in demand.
Scheduled scaling actions do not provide the responsiveness needed for fluctuating workloads, as they are based on predefined schedules rather than real-time performance metrics.
I plan that the answer is:
Use a target tracking policy to dynamically scale the Auto Scaling group.
I categorize that the answer is:
Use a target tracking policy to dynamically scale the Auto Scaling group.