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PROFESSIONAL-DATA-ENGINEER · Question #271

Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from…

The correct answer is B. Insert each new CDC record and corresponding operation type to a staging table in real time. D. Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table. Explanation/Reference: https://cloud.google.com/architecture/database-replication-to-bigquery-using-change-data-capture

Submitted by khalil_dz· Mar 30, 2026Designing data processing systems

Question

Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of CDC so that changes to the source systems are available to query in BigQuery in near-real time using log-based CDC streams, while also optimizing for the performance of applying changes to the data warehouse. Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)

Options

  • APerform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
  • BInsert each new CDC record and corresponding operation type to a staging table in real time.
  • CPeriodically DELETE outdated records from the reporting table.
  • DPeriodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
  • EInsert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest

How the community answered

(48 responses)
  • A
    29% (14)
  • B
    48% (23)
  • C
    6% (3)
  • E
    17% (8)

Explanation

Explanation/Reference: https://cloud.google.com/architecture/database-replication-to-bigquery-using-change-data-capture

Topics

#change data capture#BigQuery MERGE#staging tables#near-real-time replication

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