A company has a workload that is sending log data to Amazon CloudWatch Logs.

To analyze and visualize custom statistics such as the p90 latency (90th percentile), a CloudWatch metric must be generated from the log data. The correct method is to create a metric filter that extracts the latency value from each log event and publishes it as a CloudWatch metric. Once the metric is published, percentile statistics (p90, p95, etc.) can be displayed in CloudWatch dashboards or alarms.

AWS documentation states:

“You can use metric filters to extract numerical fields from log events and publish them as metrics in CloudWatch. CloudWatch supports percentile statistics such as p90 and p95 for these metrics.”

Contributor Insights (Option A) is for analyzing frequent contributors, not numeric distributions. Subscription filters (Option C) are used for log streaming, and Application Insights (Option D) provides monitoring of application health but not custom p90 statistics. Hence, Option B is the CloudOps-aligned, minimal-overhead solution for percentile latency monitoring.