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GCP Professional Data Engineer Practice Question

Your team operates a Google Cloud Dataflow streaming pipeline that enriches click-stream events and writes the results to BigQuery. Last night a zonal outage caused several workers in the job to crash, but the pipeline kept running and automatically recovered once the zone became available again. A junior engineer proposes adding a Cloud Scheduler job that restarts the entire pipeline whenever a worker fails to "guarantee" availability. Considering Dataflow's built-in fault-tolerance features, what is the most appropriate response?

  • Schedule a nightly full pipeline restart to flush any partial state left by failed workers and avoid duplicate output rows.

  • Explain that Dataflow already detects worker loss and transparently spins up replacement workers, so no additional restart logic is required.

  • Implement the proposed Cloud Scheduler job to shut down and relaunch the pipeline whenever a worker failure log entry appears.

  • Add custom retry loops inside every DoFn to catch exceptions and pause processing until the failed worker is manually replaced.

GCP Professional Data Engineer
Maintaining and automating data workloads
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