How to mitigate zonal failures in Dataflow?
Short answer
C Specify a worker region by using the --region flag.
Question
Key Takeaways
The question tests knowledge of Dataflow regional deployment for high availability, distinguishing it from zonal deployment to avoid the trap of manual redundancy.
To ensure high availability for Dataflow batch jobs, you should specify a worker region rather than a specific zone. The community consensus confirms that using the --region flag allows Google Cloud to automatically distribute resources across multiple zones, mitigating the risk of zonal failures.
Common mistake: Selecting Option A is a common error because it suggests manually duplicating pipelines, which is inefficient compared to using the built-in resilience of regional configuration.
Expert Analysis
Why the Answer Is Correct
Specifying a worker region using the --region flag is the correct approach because it allows the Dataflow service to automatically manage the distribution of worker resources across multiple availability zones within that region. This configuration ensures that if a specific zone experiences a failure, the pipeline can continue processing using resources in the remaining healthy zones, thereby providing fault tolerance at the job submission time.
Why the Other Options Are Wrong
Option A is incorrect because manually submitting duplicate pipelines in different zones adds operational overhead and complexity without leveraging the service's native high-availability capabilities. Option B, while good practice for storage, does not inherently control the placement of compute workers to prevent zonal failures. Option D is incorrect because Eventarc is used for event-driven architectures and triggering workflows based on events, not for managing the resilience of batch job submissions against infrastructure failures.
Community Comment Notes
Community comments overwhelmingly support Option C, citing the official Google Cloud documentation on pipeline workflows and zonal failures. Users emphasize that relying on the platform to manage resource distribution via the region flag is the standard best practice for achieving resilience in Dataflow jobs.
Official Reference
https://cloud.google.com/dataflow/docs/guides/pipeline-workflows#zonal-failures
This link opens the official vendor documentation in a new tab.
Exam Strategy
When facing questions about infrastructure resilience on Google Cloud, prioritize options that utilize regional resources over zonal ones. Remember that platform-managed redundancy is generally preferred over manual duplication or custom retry logic.
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 6 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 6
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Matt_108 👍 10 Answered COption C: https://cloud.google.com/dataflow/docs/guides/pipeline-workflows#zonal-failures
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raaad 👍 6 Answered C- Specifying a worker region (instead of a specific zone) allows Google Cloud's Dataflow service to manage the distribution of resources across multiple zones within that region
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scaenruy 👍 2 Answered CC. Specify a worker region by using the --region flag.
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Pime13 👍 1 Answered CC. Specify a worker region by using the --region flag. This ensures that your Dataflow job is submitted to a region rather than a specific zone, providing higher availability and resilience against zonal failures https://cloud.google.com/dataflow/docs/guides/pipeline-workflows#zonal-failures
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Sofiia98 👍 1 Answered Chttps://cloud.google.com/dataflow/docs/guides/pipeline-workflows#zonal-failures
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JyoGCP 👍 1 Answered COption C
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