AWS Certified Solutions Architect – Associate (SAA-C02) — Question 428
A company is running an application on Amazon EC2 instances. Traffic to the workload increases substantially during business hours and decreases afterward.
The CPU utilization of an EC2 instance is a strong indicator of end-user demand on the application. The company has configured an Auto Scaling group to have a minimum group size of 2 EC2 instances and a maximum group size of 10 EC2 instances.
The company is concerned that the current scaling policy that is associated with the Auto Scaling group might not be correct. The company must avoid over- provisioning EC2 instances and incurring unnecessary costs.
What should a solutions architect recommend to meet these requirements?
Answer options
- A. Configure Amazon EC2 Auto Scaling to use a scheduled scaling plan and launch an additional 8 EC2 instances during business hours.
- B. Configure AWS Auto Scaling to use a scaling plan that enables predictive scaling. Configure predictive scaling with a scaling mode of forecast and scale, and to enforce the maximum capacity setting during scaling.
- C. Configure a step scaling policy to add 4 EC2 instances at 50% CPU utilization and add another 4 EC2 instances at 90% CPU utilization. Configure scale-in policies to perform the reverse and remove EC2 instances based on the two values.
- D. Configure AWS Auto Scaling to have a desired capacity of 5 EC2 instances, and disable any existing scaling policies. Monitor the CPU utilization metric for 1 week. Then create dynamic scaling policies that are based on the observed values.
Correct answer: B
Explanation
Predictive scaling uses machine learning to analyze historical traffic patterns and proactively scale capacity ahead of anticipated demand, which prevents over-provisioning and optimizes costs. Enforcing the maximum capacity setting ensures that the application never scales past the limit of 10 EC2 instances. Other methods like scheduled or step scaling do not dynamically adapt to traffic fluctuations as efficiently and carry a higher risk of either under-provisioning or over-provisioning.