Google Professional Data Engineer Question 271
Select 3Google Cloud PlatformYou are designing a data processing workflow that must run reliably across multiple regions to ensure high availability and fault tolerance. The job processes high volumes of data using Google Cloud Dataflow and writes results to BigQuery. Which of the following practices should you implement to achieve the desired outcome?
- A
Use regional endpoints for Dataflow jobs and distribute workloads across multiple regions.
- B
Configure BigQuery datasets with multi-region locations to ensure data availability across regions.
- C
Set up a single-zone Dataflow job to reduce complexity and avoid cross-region network costs.
- D
Enable automatic retries for Dataflow jobs to handle transient failures.
- E
Use zonal BigQuery datasets to improve latency for specific regions.
Show answer and explanation
Correct answers: A, B, D
Explanation
To achieve high availability and fault tolerance for a data processing workflow running in multiple regions, it is essential to use regional endpoints for distributed processing, store data in multi-region BigQuery datasets, and enable automatic retries for handling transient failures. These practices ensure that the workflow remains resilient to regional outages and minimizes disruptions.
- A. Correct.
Using regional endpoints for Dataflow jobs and distributing workloads across multiple regions ensures that the processing remains available even if a specific region experiences downtime.
- B. Correct.
Configuring BigQuery datasets with multi-region locations ensures that the data is stored redundantly across multiple regions, enhancing data availability and resilience.
- C. Incorrect.
Setting up a single-zone Dataflow job reduces complexity but does not provide fault tolerance across regions. If the zone experiences an outage, the job will fail.
- D. Correct.
Enabling automatic retries for Dataflow jobs helps handle transient failures and improves job reliability, especially in distributed environments.
- E. Incorrect.
Using zonal BigQuery datasets might improve latency for specific regions but does not contribute to fault tolerance or high availability across multiple regions.