AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A company runs a global e-commerce application on a fleet of Amazon EC2 instances managed by an Auto Scaling group behind an Application Load Balancer (ALB). The application, composed of several microservices, is experiencing intermittent periods of high latency. A review of standard Amazon CloudWatch metrics for the EC2 instances, such as CPUUtilization and NetworkIn/Out, shows no correlation with the reported latency spikes. The operations team needs a monitoring strategy to quickly identify the specific transactions, users, or application components that are contributing most to these performance issues.
Which CloudWatch-based approach would be the MOST effective for this purpose?
Configure CloudWatch Anomaly Detection on the TargetResponseTime and RequestCount metrics for the Application Load Balancer. Create a CloudWatch Alarm that triggers an SNS notification to the operations team whenever an anomaly is detected, providing a link to a runbook for manual investigation.
Install the CloudWatch agent on all EC2 instances to stream application logs to CloudWatch Logs. Create a CloudWatch Logs Insights query that parses the logs to calculate transaction durations, and schedule the query to run every minute to alert the team of any anomalies.
Increase the detail level of EC2 monitoring to 1-minute frequency for all instances. Create a composite alarm that triggers if the average CPUUtilization across the fleet is normal but the ALB's TargetResponseTime p99 latency metric exceeds a predefined threshold.
Enable Application Load Balancer (ALB) access logs and send them to CloudWatch Logs. Create CloudWatch Contributor Insights rules to analyze the 'target_processing_time' and 'request' fields from the access logs. Use the resulting reports to identify the top N contributors causing the highest latency.
The correct answer is to use CloudWatch Contributor Insights on ALB access logs. CloudWatch Contributor Insights is specifically designed to analyze high-cardinality data, like log files, to identify the top contributors impacting system performance. By analyzing the target_processing_time field in the ALB access logs, this solution directly addresses the problem of identifying which specific requests or clients are causing the latency spikes, without requiring complex, manual query writing. This approach is the most direct and efficient method for root cause analysis in this scenario.
The option to use CloudWatch Logs Insights is plausible but less effective. While Logs Insights is a powerful tool for ad-hoc log analysis, it requires manually crafting and scheduling queries. Contributor Insights automates the 'top-N' contributor analysis, making it faster and more efficient for this specific use case.
The anomaly detection option is useful for alerting that a problem is occurring but does not provide insight into the cause. The team would still need to perform a manual investigation to find the root cause, which does not meet the requirement to identify the specific contributors.
Increasing the EC2 monitoring frequency and creating a composite alarm is also a reactive approach. While it can alert the team to the condition (high latency without high CPU), it does not help identify the source of the problematic requests, which is the core requirement stated in the question.
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AWS Certified Solutions Architect Professional SAP-C02
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