SOA-C02 Question 33
Single answerYour organization uses an Auto Scaling group to handle traffic spikes for a web application hosted on Amazon EC2. Recently, users reported degraded performance during high traffic periods. Upon reviewing CloudWatch metrics, you notice that the CPU utilization of instances in the Auto Scaling group frequently exceeds 85% during these periods. How should you remediate this issue to ensure the application remains highly available and performs optimally during traffic spikes?
- A
Increase the maximum number of instances in the Auto Scaling group.
- B
Modify the Auto Scaling policy to launch new instances when CPU utilization exceeds 75%.
- C
Enable Elastic Load Balancing to distribute traffic across instances.
- D
Increase the instance type in the Auto Scaling group to a larger instance size.
Show answer and explanation
Correct answer: B
Explanation
The key issue is the high CPU utilization of the instances in the Auto Scaling group during periods of high traffic. Adjusting the Auto Scaling policy to scale out proactively when CPU utilization exceeds 75% ensures that additional instances are launched in time to handle the increased load. This approach addresses the root cause of the issue and maintains application availability and performance.
- A. Incorrect.
This action alone will not address the issue unless the scaling policies are updated to respond to high CPU utilization. It increases the capacity but does not adjust the trigger thresholds.
- B. Correct.
This is the correct answer because it adjusts the scaling policy to proactively add instances before CPU utilization surpasses critical levels, ensuring the application can handle traffic spikes effectively.
- C. Incorrect.
While enabling Elastic Load Balancing is a good practice for distributing traffic, it does not directly address the high CPU utilization issue.
- D. Incorrect.
Upgrading to a larger instance type can improve performance, but it may not be the most cost-effective solution and does not address scaling dynamically based on demand.