SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company runs a critical workload on a fleet of Amazon EC2 instances behind an Application Load Balancer. The workload is latency-sensitive and the operations team wants to continuously improve performance without changing the application code. They have enabled detailed monitoring and AWS X-Ray tracing. Which solution will provide the MOST actionable, near-real-time insight into which downstream dependencies are causing increased response times?
⚠ Common exam trap
The trap here is assuming that infrastructure metrics or network flow logs can substitute for request-level tracing when the goal is to identify slow downstream dependencies.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS X-Ray service maps and trace analytics to identify latency contributions from downstream services and analyze response time distributions.
X-Ray tracing already enabled in the environment produces service maps and trace analytics that attribute latency to specific downstream calls. This lets the team continuously identify and improve the slowest dependencies without modifying application code. Host-level metrics, flow logs, and API audit trails do not provide the needed request-level timing detail.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable VPC Flow Logs on the instances' subnets and query the logs with Amazon Athena to find the slowest network flows.
Why it's wrong here
VPC Flow Logs capture IP-level metadata such as accepted and rejected packets, not application-level timing or downstream service latency. They cannot attribute response time to a specific downstream dependency. Although useful for connectivity troubleshooting, this approach would not reveal which service call is slow and is therefore not actionable for continuous performance improvement.
- ✗
Configure Amazon CloudWatch detailed monitoring for the EC2 instances and create dashboards for CPU and memory utilization.
Why it's wrong here
Detailed monitoring provides one-minute metrics for the instances, but it does not trace requests through downstream services. CPU and memory utilization are host-level indicators and may be normal even when a downstream dependency is slow. This option cannot identify which dependency contributes to increased response times, so it fails the requirement for actionable, near-real-time insight.
- ✓
Use AWS X-Ray service maps and trace analytics to identify latency contributions from downstream services and analyze response time distributions.
Why this is correct
X-Ray service maps visualize each downstream call and segment latency, letting the team pinpoint which dependency adds the most time. Trace analytics aggregates response time distributions and error rates, so improvements can be prioritized without code changes. This directly satisfies near-real-time insight into downstream dependencies and is the intended use of X-Ray tracing already enabled.
- ✗
Enable AWS CloudTrail data events on the load balancer and analyze the logs with Amazon CloudWatch Logs Insights.
Why it's wrong here
CloudTrail records API activity in the AWS account, not application request latency or downstream service calls. Data events on the load balancer are not a supported way to measure response times. Using CloudWatch Logs Insights on those logs would not expose which dependency is slow, making this option ineffective for the stated performance objective.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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