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Designing Data Processing SystemshardMultiple ChoiceObjective-mapped

PDE Designing Data Processing Systems Practice Question

A data pipeline uses Cloud Data Fusion to perform ETL jobs. The pipeline reads from BigQuery, transforms data using Wrangler, and writes to Cloud Storage. The team notices that the pipeline runs slower than expected. They suspect the Data Fusion instance is under-provisioned. Which action should be taken to improve performance?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Change the Data Fusion instance type from Basic to Enterprise

Cloud Data Fusion uses Dataproc clusters for execution. The instance type (basic, standard, enterprise) determines the Dataproc cluster configuration. Upgrading to a higher edition or increasing the number of worker nodes directly improves throughput. Wrangler transforms are executed on the Dataproc cluster, so more workers help.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Add more Dataproc Metastore instances

    Why it's wrong here

    Dataproc Metastore is for Hive metadata, not for improving Data Fusion pipeline performance.

  • Change the Data Fusion instance type from Basic to Enterprise

    Why this is correct

    Enterprise edition provides a larger default Dataproc cluster and more powerful execution environment, improving performance for heavy ETL workloads.

  • Enable Data Fusion accelerator for BigQuery

    Why it's wrong here

    There is no Data Fusion accelerator for BigQuery. Data Fusion can use Dataproc's BigQuery connector, but that's not an accelerator.

  • Rewrite the pipeline using Cloud Dataprep instead

    Why it's wrong here

    Dataprep is a different tool for data preparation, not a replacement for Data Fusion ETL. It would not necessarily improve performance.

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