A company uses an Auto Scaling group with a dynamic scaling policy based on a custom CloudWatch metric. After a recent deployment, the metric spikes unexpectedly, causing the Auto Scaling group to launch several EC2 instances. The operations team wants to quickly determine whether the spike was caused by a real load increase or a deployment issue. What is the MOST efficient way to investigate this?
Trap 1: Check the SNS topic that the scaling policy publishes to for…
SNS notifications only indicate that scaling occurred, not why.
Trap 2: Use AWS CloudTrail to review API calls that modified the scaling…
CloudTrail logs API calls but does not show application-level metrics or deployment issues.
Trap 3: Temporarily disable the scaling policy and manually increase the…
This is a reactive fix that does not help determine the root cause of the spike.
- A
Check the SNS topic that the scaling policy publishes to for notifications.
Why wrong: SNS notifications only indicate that scaling occurred, not why.
- B
Use CloudWatch Logs Insights to query application logs for error patterns or deployment markers that coincide with the metric spike.
CloudWatch Logs Insights allows querying logs to find patterns related to the spike.
- C
Use AWS CloudTrail to review API calls that modified the scaling policy.
Why wrong: CloudTrail logs API calls but does not show application-level metrics or deployment issues.
- D
Temporarily disable the scaling policy and manually increase the desired capacity to handle the load.
Why wrong: This is a reactive fix that does not help determine the root cause of the spike.