Google Cloud Certified Professional Cloud Developer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A1

  Correct answer A: Batching Rows: By batching multiple rows into a single request, you minimise the overhead of network communication and API call latency. BigQuery's InsertAllRequest supports inserting multiple rows in a single API call. Parallel Inserts (Option B): While parallel processing can improve performance, it introduces complexity and may require handling concurrency issues. It's generally better to batch rows first. Writing to Cloud Storage (Options C & D): Writing data to Cloud Storage and then loading it into BigQuery can be efficient for very large datasets, but it adds extra steps and complexity. It's more suitable for bulk load operations rather than small, frequent inserts.

• 1

  For smaller datasets or when simplicity is paramount: Including multiple rows with each request is often sufficient. For larger datasets or when performance is critical: Parallel inserts are the way to go.

• Chose A1

  A. Include multiple rows with each request: This would be a very efficient way to batch the insert operations. BigQuery's insertAll method supports batched inserts, so instead of inserting each row in a separate request, you could group multiple rows into a single insertAll request. This approach reduces the number of HTTP requests made to the BigQuery service, which can improve throughput and reduce the risk of hitting rate limits.