What's changed: Added per-section figures (cert-figure-retrofit). New AI-200 Chapter 4 (secret management = Key Vault/App Configuration/managed identity keyless/RBAC, observability = OpenTelemetry instrumentation/distributed tracing/Application Insights·Azure Monitor/KQL/AI-specific monitoring = tokens·cost·quota·generation quality)
4.2Observability (Monitoring, Tracing, Logs)
Understand observability for a distributed AI back-end—instrumentation with OpenTelemetry, distributed tracing, Application Insights / Azure Monitor, log analysis with KQL, and monitoring tokens/cost/quality.
In an event-driven, distributed AI pipeline, a single request is processed across multiple services. Design observability so that when something goes wrong you can trace "where and why it is slow/failing." The standard is instrumentation with OpenTelemetry (embedding traces, metrics, and logs in code), collected into Application Insights / Azure Monitor.
4.2.1Distributed tracing and log analysis
Distributed tracing stitches the per-service work (spans) of one request into a single trace, so you can see bottlenecks like "embedding is fast but the AI call is stalling." Query the collected logs/metrics with KQL (Kusto Query Language) to analyze error rates, latency distributions, and specific errors. Set threshold alerts to catch anomalies early.
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