GCP Professional Data Engineer Practice Question

A central data team needs to share a BigQuery dataset that stores global product-sales facts with five subsidiary companies. Each subsidiary uses a separate Google Cloud project tied to its own billing account. The solution must avoid copying the underlying data, ensure that every subsidiary pays for the queries it runs, allow the central team to retain full control over schema changes, and prevent all subscribers from accessing the "unit_cost" column. Which approach satisfies all of these requirements?

Grant each subsidiary service account BigQuery Data Viewer and BigQuery Job User roles on the sales dataset, then schedule a daily job that writes a view without the "unit_cost" column into each subsidiary project so their queries are billed locally.
Create a private data exchange in Analytics Hub, publish a listing for the sales dataset, grant each subsidiary the analyticshub.subscriber role on the exchange, and apply a policy-tag rule that blocks access to the "unit_cost" column. Each subsidiary subscribes to the listing, receives a read-only linked dataset in its own project, and runs queries that are billed to its own billing account.
Build a materialized view that excludes the "unit_cost" column, copy that view into every subsidiary project by using BigQuery Data Transfer Service, and manage dataset-level IAM so each subsidiary's queries are billed to its own project.
Export the sales tables to Cloud Storage every day, share the bucket with each subsidiary, and instruct them to create external tables that omit the "unit_cost" column. Rely on dual-region storage billing for cost separation.

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Answer Description

Publishing the dataset through a private Analytics Hub data exchange allows the central team to expose the data once while keeping physical storage in the publisher's project. After each subsidiary accepts the listing, BigQuery automatically creates a read-only linked dataset in the subscriber's project; any queries are executed (and billed) in that subscriber's project, so costs are isolated. Because the linked dataset simply points to the source, the publisher can change the underlying tables or schema at any time without affecting control. Applying a BigQuery policy-tag-based column-level access policy to the unit_cost column in the source dataset prevents all subscribers from viewing that column, and this restriction is enforced transparently on the linked datasets. Granting the analyticshub.subscriber role on the data exchange allows each subsidiary to subscribe, and giving them BigQuery Job User (or User) on their own projects lets them submit query jobs.

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