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Answer: Insert each new CDC record and corresponding operation type to a staging table in real time., Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
**B** and **D** are the correct answers. - **B**: Inserting CDC records into a staging table in real time captures changes immediately with minimal latency, and batch processing reduces compute overhead. - **D**: Using DML MERGE periodically consolidates multiple operations into a single transaction, efficiently managing changes and reducing compute overhead. Other options are less optimal: - **A**: Deleting outdated records periodically can introduce latency and additional processing overhead. - **C**: Processing each CDC record individually on the reporting table increases compute overhead and latency. - **E**: This approach adds unnecessary complexity and may not efficiently minimize latency or reduce compute overhead.
Author: LeetQuiz Editorial Team
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Your company is moving from on-premises data warehousing to BigQuery, aiming to enhance Change Data Capture (CDC) processes for real-time updates. The goal is to minimize latency in reflecting these changes in BigQuery reporting tables while reducing compute overhead. Which two strategies should you implement? (Select two.)
A
Periodically DELETE outdated records from the reporting table.
B
Insert each new CDC record and corresponding operation type to a staging table in real time.
C
Perform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
D
Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
E
Insert 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 version of each unique record.
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