Datadog APM & Distributed Tracing Fundamentals case studies
Datadog APM & Distributed Tracing Fundamentals · Put your knowledge to work in context.
The Datadog APM & Distributed Tracing Fundamentals certification is Datadog's introductory credential for its Application Performance Monitoring product. Datadog describes it as an introductory APM exam that tests base knowledge of the APM product, and lists its coverage as APM fundamentals, application instrumentation with Datadog, insight discovery with Datadog APM, visualizing insights, and troubleshooting applications using APM. It suits engineers, SREs, and platform or observability practitioners who already work with instrumented services and want independent confirmation that they can install, configure, and use APM features in practice. There are no formal prerequisites; Datadog states the exam is open to everyone, and that candidates do best with at least a few months of hands-on Datadog experience with live systems. The Datadog APM & Distributed Tracing Fundamentals case studies are 5 cases and about 3,839 words.
1The trace that stops at the queue
A retail platform traces order placement across four services: api-gateway (Go) calls orders (Java, Spring Boot), which publishes to an SQS queue called order-events; fulfillment-worker (Python) consumes those messages and calls inventory (Java). Volume is roughly 1.4 million order requests per day.
926 words, about 5 min2Fifty-two thousand resource names
storefront-api is an Express application running on 40 Kubernetes pods. It was instrumented with dd-trace-js at startup and no per-route configuration.
698 words, about 3 min3The canary with a flat median
payments is a Java service being rolled out as a 15/85 canary between v2.3.0 and v2.2.14. Median latency is unchanged at 118 ms for both versions and the error rate is flat at 0.4%.
716 words, about 4 min4Three widgets that disagree
An SRE has inherited a 'Payments SLO' dashboard and must make it trustworthy before a quarterly review. Three widgets are in dispute.
727 words, about 4 min5The 3.6 second gap
checkout is a Java service on 12 Kubernetes pods, each limited to 2 vCPU with a 1.5 GB heap, serving about 40 requests per second. Roughly every nine minutes, p99 latency rises from 900 ms to 4.2 s for about 90 seconds and then recovers.
772 words, about 4 min