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GCP Professional Data Engineer Practice Question

Your company processes 12 TB of IoT sensor data every night in an on-prem Hadoop cluster using PySpark jobs. They must move to Google Cloud but keep the existing Spark code, avoid vendor lock-in so they can repatriate workloads later, and prefer open-source orchestration. Which Google Cloud-based design best meets these portability requirements while adding the ability to scale on demand?

  • Rewrite the pipelines in Apache Beam and execute them on Dataflow, scheduling executions with Workflows.

  • Use autoscaling Dataproc Spark clusters that read and write Parquet files in Cloud Storage, orchestrated end-to-end with Cloud Composer DAGs.

  • Containerize each Spark job and deploy on Cloud Run, triggering executions via Pub/Sub and coordinating with Cloud Scheduler.

  • Load historical data into BigQuery, stream new data with the BigQuery Streaming API, and schedule nightly SQL transformations with Dataform.

GCP Professional Data Engineer
Designing data processing systems
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