How to stream 1.5 GB/s to BigQuery with exactly-once semantics?

You are using a Dataflow streaming job to read messages from a message bus that does not support exactly-once delivery. Your job then applies some transformations, and loads the result into BigQuery. You want to ensure that your data is being streamed into BigQuery with exactly-once delivery semantics. You expect your ingestion throughput into BigQuery to be about 1.5 GB per second. What should you do?

  1. Use the BigQuery Storage Write API and ensure that your target BigQuery table is regional.
  2. Use the BigQuery Storage Write API and ensure that your target BigQuery table is multiregional. Source Reference Answer
  3. Use the BigQuery Streaming API and ensure that your target BigQuery table is regional.
  4. Use the BigQuery Streaming API and ensure that your target BigQuery table is multiregional.

Community Votes

B
57%
A
43%

57% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The question tests both the exactly-once delivery capability of the Storage Write API and the throughput limitations of BigQuery locations; the trap is assuming regional tables are faster when they actually have a lower streaming quota.

To achieve exactly-once delivery into BigQuery from a Dataflow streaming job at 1.5 GB/s, use the BigQuery Storage Write API with a multiregional table. The community consensus (B) is that only multiregional tables offer the required 3 GB/s throughput, while regional tables are limited to 300 MB/s.

Option A (Storage Write API + regional table) is the most common wrong answer; it overlooks that regional tables only support 300 MB/s write throughput, which cannot handle the required 1.5 GB/s.

Community Discussion (16 comments)

AlizCert 👍 16 Selected: B
It should B, Storage Write API has "3 GB per second throughput in multi-regions; 300 MB per second in regions"
raaad 👍 13 Selected: A
  • BigQuery Storage Write API: This API is designed for high-throughput, low-latency writing of data into BigQuery. It also provides tools to prevent data duplication, which is essential for exactly-once delivery semantics. - Regional Table: Choosing a regional location for the BigQuery table could potentially provide better performance and lower latency, as it would be closer to the Dataflow job if they are in the same region.
Siahara 👍 2 Selected: A
A. Implement the BigQuery Storage Write API and guarantee that the target BigQuery table is regional. Here's the breakdown: Why Option A is Superior Exactly-Once Delivery: The BigQuery Storage Write API intrinsically supports exactly-once delivery using stream offsets. This guarantees that each message is written to BigQuery exactly one time, even in the case of retries due to the lack of native exactly-once support in your message bus. High Throughput: The Storage Write API is optimized for high-throughput scenarios. It can handle the expected ingestion throughput of 1.5 GB per second. Regional Tables: Using a regional BigQuery table aligns with best practices when utilizing the Storage Write API, as it helps to minimize latency and reduce potential cross-region communication costs.
juliorevk 👍 1 Selected: B
  • BigQuery Storage Write API: This API is designed for high-throughput, low-latency writing of data into BigQuery. It also provides tools to prevent data duplication, which is essential for exactly-once delivery semantics. - The multiregional table ensures that your data is highly available and can be streamed into BigQuery across multiple regions. It is better suited for high-throughput and low-latency workloads, as it provides distributed write capabilities that can handle large data volumes, such as the 1.5 GB per second you expect to stream.
Pime13 👍 1 Selected: A
https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery For new projects, we recommend using the BigQuery Storage Write API instead of the tabledata.insertAll method. The Storage Write API has lower pricing and more robust features, including exactly-once delivery semantics https://cloud.google.com/bigquery/docs/write-api#advantages
hussain.sain 👍 1 Selected: B
B is correct. When aiming for exactly-once delivery in a Dataflow streaming job, the key is to use the BigQuery Storage Write API, as it provides the capability to handle large-scale data ingestion with the correct semantics, including exactly-once delivery.
himadri1983 👍 2 Selected: B
3 GB per second throughput in multi-regions; 300 MB per second in regions https://cloud.google.com/bigquery/quotas#write-api-limits
m_a_p_s 👍 2 Selected: B
streamed into BigQuery with exactly-once delivery semantics >>> Storage Write API ingestion throughput into BigQuery to be about 1.5 GB per second >>> multiregional (check throughput rate here >>> https://cloud.google.com/bigquery/quotas#write-api-limits)
NatyNogas 👍 1 Selected: A
  • Choosing a regional target BigQuery table ensures that data is stored redundantly in a single region, providing high availability and durability.
CloudAdrMX 👍 2 Selected: B
According to this documentation, its B https://cloud.google.com/bigquery/quotas#write-api-limits
imazy 👍 1 Selected: A
Write API support 2.5 GB / sec speed and support exactly-once delivery semantics https://cloud.google.com/bigquery/docs/write-api#connections whereas in streaming duplicates can come and needed to remove them manually https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery#dataavailability
SamuelTsch 👍 4 Selected: B
looking for this documentation https://cloud.google.com/bigquery/quotas#write-api-limits. 3 GB/s in multi-regions; 300MB/s in regions
HermanTan 👍 2
To ensure that analysts do not see customer data older than 30 days while minimizing cost and overhead, the best option is: B. Use a timestamp range filter in the query to fetch the customer’s data for a specific range. This approach directly addresses the issue by filtering out data older than 30 days at query time, ensuring that only the relevant data is retrieved. It avoids the overhead and potential delays associated with garbage collection and manual deletion processes
hanoverquay 👍 1 Selected: D
option D
Matt_108 👍 1 Selected: A
Option A
Ed_Kim 👍 2 Selected: A
Voting on A

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Expert Analysis

Why the Answer Is Correct

The BigQuery Storage Write API is the only option that provides exactly-once delivery semantics, as the older streaming API (tabledata.insertAll) only offers at-least-once. Since the Dataflow job reads from a message bus that lacks exactly-once support, the Storage Write API’s de-duplication capabilities are essential. The throughput requirement of 1.5 GB/s dictates the table location: BigQuery quotas list Storage Write API limits as 3 GB/s for multiregional locations and only 300 MB/s for regional locations. Thus, B (Storage Write API + multiregional table) satisfies both exactly-once and throughput needs.

Why the Other Options Are Wrong

Option A uses the correct API but the wrong location — a regional table’s 300 MB/s ceiling cannot handle 1.5 GB/s. Options C and D use the BigQuery Streaming API, which does not support exactly-once delivery, so they fail the core requirement. Even if D’s multiregional location gives higher throughput, the lack of exactly-once semantics makes it invalid. Therefore, only B meets both constraints.

Community Comment Notes

The majority of comments (56 votes for B) cite the official quota documentation, with comment [3] and [5] pointing directly to the 3 GB/s multiregional vs 300 MB/s regional limits. Comment [2] incorrectly argues that regional tables “could potentially provide better performance,” but that refers to latency, not throughput quotas. Comment [1] summarises the exact reason: “Storage Write API has 3 GB per second throughput in multi-regions; 300 MB per second in regions.” The official docs linked in comments confirm that Storage Write API is recommended for exactly-once and has lower pricing.

Official Reference

Exam Strategy

When a question specifies a numeric throughput requirement, always verify it against BigQuery’s published quotas before choosing a location. Remember that regional tables are not inherently faster for writes; multiregional tables have much higher write throughput limits, so don’t assume that 'regional' means better performance.

Related Analysis

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