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GCP Professional Data Engineer Practice Question

Your analytics team uses Cloud Data Fusion to load operational data into a staging dataset in BigQuery each night. SQL transformations are managed in Dataform and publish curated tables to a production dataset feeding dashboards. You need to block the transformation workflow whenever staging tables contain null customer IDs, duplicate order IDs, or negative revenue values. The solution must integrate with the existing Dataform workflow, require little custom code, and surface failures in the run logs. What should you do?

  • Create SQLX assertion files in Dataform that SELECT all rows violating each rule and tag them as tests so the Dataform run fails if any record is returned.

  • Add NOT NULL and CHECK constraints to the staging tables; rely on BigQuery to reject bad data and make Dataform fail automatically.

  • Set up a Cloud Scheduler job that triggers a Cloud Function to run custom validation queries after each load; if any query finds bad data, publish a Pub/Sub message that cancels the Dataform run via Cloud Build.

  • Configure a Cloud Monitoring alert on the nightly load row count; send an incident to the on-call team and rerun the Dataform workflow manually when alerted.

GCP Professional Data Engineer
Designing data processing systems
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