GCP Professional Cloud Architect Practice Question
Your company runs Java microservices on GKE across two regions. Users report 99th-percentile latencies above the 500 ms SLO, yet CPU and memory metrics look normal. You already collect structured logs and system metrics with Cloud Logging and Monitoring. The team needs a Google Cloud service that can follow a single request through every microservice, highlight the slow hop, and display the request's critical path in near real time. Which service should you enable?
Cloud Trace with application instrumentation (for example, via OpenTelemetry)
Exporting a logs-based metric to BigQuery for custom latency analysis
Error Reporting to aggregate and inspect stack traces across services
Cloud Profiler to collect continuous CPU and memory flame graphs
Cloud Trace is Google Cloud's distributed tracing service. When applications are instrumented -for example with OpenTelemetry-Trace collects end-to-end latency data for individual requests, even as they cross service or network boundaries. The Trace Explorer visualizes each request's critical path and pinpoints the slowest spans, giving near-real-time insight into bottlenecks. Cloud Profiler samples CPU and memory usage but does not correlate data to individual requests. BigQuery log exports can ingest data in near real time, but rebuilding traces requires custom SQL and dashboards rather than an out-of-the-box visualization. Error Reporting aggregates uncaught exceptions and stack traces but does not measure request latency.
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What is distributed tracing?
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What is OpenTelemetry, and how does it work with Cloud Trace?
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Why is Cloud Trace better than BigQuery for latency analysis?
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What is OpenTelemetry and how does it help in application instrumentation?
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How does Cloud Trace display the request’s critical path?
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Why is Cloud Profiler not suitable for tracking request latency issues?
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