Google Professional Data Engineer Question 227
Single answerGoogle Cloud PlatformYour organization uses Google Cloud Dataflow to process large volumes of business-critical data in near real-time. During peak processing times, you notice that jobs are delayed due to insufficient resources, which affects downstream systems. What should you do to ensure that enough resources are available to handle the workload reliably?
- A
Enable autoscaling for the Dataflow job to dynamically adjust worker instances based on workload.
- B
Manually increase the number of worker instances before running the Dataflow job.
- C
Switch to a fixed-size cluster to prevent resource allocation delays.
- D
Use Preemptible VM instances to reduce costs and scale up resources as needed.
Show answer and explanation
Correct answer: A
Explanation
To ensure resource availability for business-critical data processes like Dataflow jobs, enabling autoscaling is the best solution. Autoscaling dynamically adjusts the number of worker instances based on demand, ensuring that the system can handle peak workloads without manual intervention. This approach is both efficient and reliable for maintaining performance and avoiding delays.
- A. Correct.
Enabling autoscaling allows Dataflow to automatically adjust the number of worker instances based on the workload, ensuring sufficient resources during peak times. This is the best approach for handling unpredictable workloads.
- B. Incorrect.
Manually increasing the number of worker instances may provide more resources, but it is not efficient or reliable for handling dynamic and unpredictable workloads.
- C. Incorrect.
Using a fixed-size cluster might prevent resource scaling delays, but it is not flexible enough to handle variable workloads and can lead to over-provisioning or under-provisioning of resources.
- D. Incorrect.
Preemptible VM instances are cost-effective but are not guaranteed to be available at all times, making them unsuitable for business-critical, latency-sensitive workloads.