Crucial Exams

GCP Professional Data Engineer Practice Question

After migrating a 40-TB Oracle data mart to BigQuery using Datastream (CDC -> Cloud Storage) and Dataflow loads, you must prove before cut-over that every source row matches its BigQuery copy. The solution has to 1) scale across hundreds of tables without per-table coding, 2) surface any row-level mismatches, and 3) expose results to Cloud Monitoring for alerts. Which approach best meets these requirements?

  • Develop individual Dataflow pipelines for each table that calculate row hashes in Oracle and BigQuery, then compare the results and publish a metric.

  • Enable a built-in Datastream data-validation feature to generate checksum comparisons automatically and send the results to Cloud Logging.

  • Create final BigQuery snapshots and run manual EXCEPT queries against exported Oracle CSV files; record any differences in a spreadsheet.

  • Run Google's open-source Data Validation Tool as a Dataflow flex template to compute per-table checksums between Oracle and BigQuery, log results to Cloud Logging, and create log-based metrics for Cloud Monitoring alerts.

GCP Professional Data Engineer
Designing data processing systems
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