GCP Professional Cloud Architect Practice Question

Your company runs a fleet of Compute Engine VMs and several Cloud Run microservices behind an external HTTP(S) load balancer. Site-reliability engineers need a single dashboard that shows CPU and memory for every workload, an alert when log entries containing the text "payment_failed" exceed 50 per minute, and end-to-end distributed traces that reveal where most request latency is spent. Which approach best satisfies these requirements while keeping operational overhead low?

Deploy Prometheus sidecars in every service for metrics, forward logs to an Elasticsearch cluster with Fluentd, use Jaeger for tracing, and present data through a custom Grafana portal.
Install only the Cloud Monitoring metric agent on VMs, configure uptime checks for CPU and memory, set an alert on profiler-reported exceptions exceeding 50 per minute, and rely on Cloud Profiler flame graphs to locate latency sources.
Enable Cloud Operations on all resources: deploy the Ops Agent to each VM, rely on Cloud Run's built-in telemetry, create a logs-based metric filtered on "payment_failed" with an alerting policy, and enable the Cloud Trace API with language agents to emit distributed spans.
Export every log entry to BigQuery, build Data Studio dashboards for CPU and memory from scheduled queries, run periodic SQL jobs to count "payment_failed" lines, and inspect query execution plans to diagnose request latency.

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Answer Description

Installing the Ops Agent on each Compute Engine VM streams host metrics and logs directly to Cloud Monitoring and Cloud Logging. Cloud Run already exports service-level metrics and container stdout/stderr to the same back-ends, so a unified dashboard can be built without additional collectors. In Cloud Logging, a logs-based metric filtered on textPayload:"payment_failed" can count matching entries; attaching an alerting policy to this metric meets the notification requirement. Finally, enabling the Cloud Trace API and adding the language-specific Trace agent or OpenTelemetry exporter to the services allows spans to be sent to Cloud Trace, producing end-to-end request traces that highlight the slowest downstream call. The other options either rely on self-managed stacks, batch processing, or tools (uptime checks, Cloud Profiler) that do not meet all three observability goals.

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