A transaction processing company has weekly scripted batch jobs that run on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group. The number of transactions can vary, but the baseline CPU utilization that is noted on each run is at least 60%. The company needs to provision the capacity 30 minutes before the jobs run. Currently, engineers complete this task by manually modifying the Auto Scaling group parameters. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. The company needs an automated way to modify the Auto Scaling group’s desired capacity. Which solution will meet these requirements with the LEAST operational overhead?
Select one answer.
Marked answer: C
Community Discussion
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
Chose C1
NOT B: - Scheduled scaling works only if you already know the required capacity. But the question says: "The company does not have the resources to analyze the required capacity trends."
Chose C1
The reason why I picked C, and not B, is because: "The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts". If you would use B, you have to manually analyze min and max to always have that window, with C it will be done by AWS + we already have a history, so should not be the problem
Chose B1
Predictive scaling is for irregular workload based on known history. (no history here) Schedulked scaling is for the workload is know (60% baseline) and planned (here once a week)
