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GCP Professional Data Engineer Practice Question

Your company runs several streaming Dataflow jobs in separate Google Cloud projects, each triggered by Cloud Composer. SREs want one place to observe end-to-end pipeline lag, receive a PagerDuty alert if lag exceeds five minutes, and inspect individual worker logs without receiving broad project-level permissions. Which architecture best satisfies these requirements while minimizing operational overhead?

  • Publish Dataflow metrics to Pub/Sub, stream them into BigQuery, use Cloud Scheduler queries to compute lag, trigger Cloud Functions to send PagerDuty alerts, and store worker logs in a BigQuery dataset shared with SREs.

  • Enable Cloud Trace in every project, export latency traces to a shared Trace project, create alerts on trace duration, and grant SREs Trace Viewer to inspect worker traces.

  • Create a central operations project, add all pipeline projects to its Cloud Monitoring metrics scope, define an alert on the Dataflow job system_lag metric (>300 s) with a PagerDuty notification channel, and configure aggregated Log Router sinks that export Dataflow worker logs to a log bucket in the operations project where SREs have Logs Viewer access.

  • Deploy Prometheus on GKE to scrape OpenCensus metrics from Dataflow workers, configure Alertmanager for paging, and set up an Elasticsearch-Kibana stack for logs with Kibana viewer access for SREs.

GCP Professional Data Engineer
Designing data processing systems
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