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GCP Professional Data Engineer Practice Question

Your company operates a shared-VPC architecture with a host project named prod-vpc. You must design a batch Dataflow pipeline that reads Parquet files from Cloud Storage and loads the results into a BigQuery dataset. Finance regulations state that the worker VMs may not have public IP addresses and that all traffic between the workers and Google APIs must stay on Google's private network. Which approach meets the requirements while adding the least operational overhead?

  • Run the Dataflow job with the --no_use_public_ips flag and specify a subnet in prod-vpc that has Private Google Access enabled; do not configure Cloud NAT or external IPs.

  • Create a Cloud NAT gateway in prod-vpc, run Dataflow workers without external IPs, and let the NAT gateway provide internet egress to Cloud Storage and BigQuery.

  • Replace the batch job with Cloud Functions that read from Cloud Storage and write to BigQuery, because Cloud Functions execute in a serverless environment without public IPs.

  • Provision a separate VPC in the data project, peer it with prod-vpc, run Dataflow in its default (public) mode, and restrict egress traffic using firewall rules that allow 0.0.0.0/0 only over HTTPS.

GCP Professional Data Engineer
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