Professional-Data-Engineer Exam Dumps | You are designing a stateful data processing pipeline that reads data from a Cloud Storage bucket and

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Question 70

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Question 70/186

You are designing a stateful data processing pipeline that reads data from a Cloud Storage bucket and writes transformed data to a BigQuery table. The pipeline must be highly available and resilient to zonal failures within the us-central1 region. You need to configure a Dataflow pipeline ensuring minimal disruption during a zonal outage. What should you do?

A. Deploy the Dataflow job to a single zone within us-central1 and configure it to use a regional persistent disk to store its state.

B. Launch the Dataflow job with the --region us-central1 parameter.

C. Deploy the Dataflow job to a single zone within us-central1 and use a multi-regional Cloud Storage bucket to store its state.

D. Launch the Dataflow job with the --zone us-central1-a parameter.

Correct Answer: B

To ensure high availability and resilience against zonal outages in Dataflow, you must leverage Regional Endpoints.
* Regional Placement: By using the --region flag (e.g., us-central1) without specifying a specific --zone, Dataflow automatically places workers in different zones within that region. If a specific zone in us- central1 fails, the Dataflow service backend can automatically restart or migrate workers to a healthy zone within the same region.
* State Management: Dataflow handles state (shuffling and persistent state) natively. By operating at the regional level, the control plane can maintain the job's continuity even if a zone goes offline.
* Correcting other options:
* A & D: Specifying a single zone (either by the --zone parameter or by manual deployment to one zone) creates a single point of failure. If that zone goes down, the pipeline stops entirely.
* C: While multi-regional GCS is highly available, it doesn't solve the compute resilience issue. If the Dataflow workers are in a single zone and that zone fails, the processing stops regardless of where the data is stored.
Reference: Google Cloud Documentation on Dataflow Regional Endpoints:
"By default, the Dataflow service runs your job in a regional endpoint. Using a regional endpoint provides high availability and geographic redundancy... If you specify a region but not a zone, Dataflow automatically selects a zone within that region to run your workers. This helps ensure that your job is resilient to zonal failures." (Source: Dataflow Regional Endpoints)
"In the event of a zonal outage, the Dataflow service attempts to schedule workers in another zone within the same region." (Source: Dataflow high availability)

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