Crucial Exams

GCP Professional Data Engineer Practice Question

Your data engineering team must orchestrate a daily batch pipeline that (1) launches two separate Dataflow jobs in parallel, (2) waits until both finish, (3) invokes a Cloud Function for data-quality validation, and (4) then loads the curated output into three BigQuery tables, one after another. The orchestration tool has to let you express these fan-out/fan-in dependencies in code, offer one-click back-fills, provide built-in task-retry policies and a graphical monitoring UI, while keeping infrastructure management to a minimum-no need to manually provision or patch scheduler clusters. Which Google Cloud service best fits these requirements?

  • Define the control flow in Google Cloud Workflows using YAML and call each service through REST APIs.

  • Deploy the pipeline as a Python-based Apache Airflow DAG in Cloud Composer, using its operators for Dataflow, Cloud Functions, and BigQuery.

  • Embed the orchestration logic inside a single batch Dataflow pipeline that invokes external services via side effects.

  • Create a set of Cloud Scheduler cron jobs that publish Pub/Sub messages to trigger Cloud Functions which, in turn, launch Dataflow templates and BigQuery loads.

GCP Professional Data Engineer
Ingesting and processing the data
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