Google Professional Data Engineer exam dumps

Google Professional Data Engineer practice question 221 of 279

Professional Data Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Data Engineer Question 221

Select 3Google Cloud Platform

You are managing a data pipeline in Google Cloud Dataflow, which processes large amounts of data daily. Your team has identified that the pipeline’s Compute Engine instances often run idle during low-traffic periods, leading to unnecessary costs. What steps can you take to optimize resources and reduce costs without impacting performance?

  1. A

    Enable autoscaling for the Dataflow job.

  2. B

    Switch to using preemptible VM instances for the pipeline.

  3. C

    Increase the number of workers to ensure the job completes faster.

  4. D

    Use Dataflow Shuffle for better resource management.

  5. E

    Configure the pipeline to run only during high-traffic periods.

Show answer and explanation

Correct answers: A, B, D

Explanation

To optimize resources and reduce costs in this scenario, enabling autoscaling ensures that the pipeline uses only the necessary resources based on the workload. Preemptible VM instances are a cost-effective option for batch jobs that can tolerate interruptions. Dataflow Shuffle further optimizes resource management by reducing the burden on worker instances. These methods collectively balance cost savings and performance without compromising the pipeline's functionality.

  • A. Correct.

    Enabling autoscaling allows Dataflow to dynamically adjust the number of workers based on the current workload, optimizing resource usage and reducing costs during low-traffic periods.

  • B. Correct.

    Using preemptible VM instances can significantly reduce costs for batch processing jobs since these instances are cheaper but suitable for fault-tolerant workloads like Dataflow pipelines.

  • C. Incorrect.

    Increasing the number of workers might reduce job completion time but could result in over-provisioning and higher costs during low-traffic periods, which does not align with the goal of optimizing resources.

  • D. Correct.

    Using Dataflow Shuffle offloads data-intensive operations to Google-managed infrastructure, improving job performance and reducing the need for additional worker resources.

  • E. Incorrect.

    Configuring the pipeline to run only during high-traffic periods may result in delays and does not align with the goal of maintaining performance while optimizing costs.

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