Jeff’s Note #
Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.
For AWS DVA-C02 candidates, the confusion often lies in how to properly integrate AWS X-Ray within ECS Fargate microservices—specifically around SDK usage and daemon communication. In production, this is about knowing exactly how your app interacts with X-Ray APIs and the daemon to enable trace collection without misconfigurations or permissions issues. Let’s drill down.
The Certification Drill (Simulated Question) #
Scenario #
A software company named NimbusApps has a microservices-based application deployed in Amazon ECS using AWS Fargate. Users start reporting intermittent errors and slow responses. To troubleshoot the root cause, the lead developer wants to enable detailed tracing with AWS X-Ray.
The Requirement #
Determine the correct set of actions a developer should take to properly instrument the microservices to capture traces in AWS X-Ray.
The Options #
- A) Deploy AWS X-Ray as a sidecar container to the microservices. Update the ECS task role policy to allow access to the X-Ray API.
- B) Deploy AWS X-Ray as a daemonset to the Fargate cluster. Update the ECS service role policy to allow access to the X-Ray API.
- C) Instrument the application by using the AWS X-Ray SDK. Update the application to use the PutXrayTrace API call to communicate directly with the X-Ray API.
- D) Instrument the application by using the AWS X-Ray SDK. Update the application to communicate with the X-Ray daemon.
- E) Instrument the ECS task to send stdout and stderr logs to Amazon CloudWatch Logs. Update the task role policy to allow the cloudwatch:PullLogs action.
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Correct Answer #
A and D
Quick Insight: The Developer Imperative #
- AWS X-Ray in ECS Fargate requires the X-Ray daemon to collect trace data from SDK-instrumented apps. Deploying X-Ray as a sidecar and communicating via the daemon is the established best practice.
- Direct API calls via PutXrayTrace (Option C) are uncommon and increase complexity and latency.
- Daemonsets do not exist as a concept for Fargate (Option B).
- CloudWatch logs (Option E) help with logging but do not aid distributed tracing directly.
Content Locked: The Expert Analysis #
You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?
The Expert’s Analysis #
Correct Answer #
Options A and D
The Winning Logic #
-
Option A: Deploying AWS X-Ray as a sidecar container inside the same Fargate task as the microservices is the recommended method since Fargate does not support running daemons on the host or daemonsets as in Kubernetes. This sidecar acts as the local X-Ray daemon, receiving trace data via UDP from the app and forwarding it to X-Ray’s backend. Updating the ECS task role to permit X-Ray API access is essential to allow the daemon to send trace data.
-
Option D: Instrumenting the application with the AWS X-Ray SDK ensures the app generates trace segments and sends them to the local X-Ray daemon (running as the sidecar). The communication with the daemon usually occurs over UDP at 127.0.0.1:2000. This pattern decouples your app from directly invoking X-Ray APIs, simplifying code and improving performance.
The Trap (Distractor Analysis) #
-
Option B: Daemonsets are a Kubernetes construct and cannot be deployed on Fargate, which abstracts away the host. Also, service roles do not control X-Ray traffic for ECS tasks; task roles do.
-
Option C: Although possible, directly calling the PutXrayTrace API from your app is discouraged because it increases coupling and complexity, defeating the purpose of the X-Ray daemon.
-
Option E: Sending stderr/stdout logs to CloudWatch is good for basic debugging but does not provide distributed tracing and won’t directly solve error traceability concerns.
The Technical Blueprint #
B) For Developer / SysOps (Code/CLI Snippet):
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"xray:PutTraceSegments",
"xray:PutTelemetryRecords"
],
"Resource": ["*"]
}
]
}
This JSON IAM policy snippet should be attached to your ECS task role to allow the X-Ray daemon (sidecar) to send trace data to AWS.
The Comparative Analysis (Developer Focus) #
| Option | API Complexity | Performance | Use Case |
|---|---|---|---|
| A | Moderate (sidecar deployment) | High (local daemon) | Recommended for ECS Fargate to run daemon |
| B | Invalid (Daemonset not on Fargate) | N/A | Kubernetes only, not applicable for Fargate |
| C | High (direct API calls) | Lower (more overhead) | Not recommended, increases complexity |
| D | Moderate (SDK + daemon) | High | Best practice for instrumenting apps |
| E | Low (logs only) | N/A | Helpful for logs, not for tracing |
Real-World Application (Practitioner Insight) #
Exam Rule #
For the AWS DVA-C02 exam, always choose instrumenting your app with the X-Ray SDK and using the daemon sidecar pattern for microservices running on ECS Fargate.
Real World #
In actual production environments, teams stick to the sidecar daemon because it offloads trace forwarding, reduces SDK complexity, and prevents throttling issues you might get with direct API calls.
(CTA) Stop Guessing, Start Mastering #
Disclaimer
This is a study note based on simulated scenarios for the AWS DVA-C02 exam.