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Ingesting and Processing the DatahardMultiple ChoiceObjective-mapped

PDE Ingesting and Processing the Data Practice Question

Your team has a Dataflow pipeline that reads from BigQuery, transforms data, and writes to GCS. The pipeline is failing with 'Out of Memory' errors on the worker nodes. The input data is large but fits within the total cluster memory. Which configuration change is most likely to resolve the issue without increasing costs significantly?

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

Use a worker machine type with more memory, such as n2-highmem.

The default Dataflow worker machine type may have insufficient memory per core for the pipeline's operations. Using a high-memory machine type (e.g., n2-highmem) increases memory per worker without necessarily increasing the number of workers, thus controlling costs.

Answer analysis

Option-by-option breakdown

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

  • Use a worker machine type with more memory, such as n2-highmem.

    Why this is correct

    High-memory machines provide more memory per core, addressing OOM.

  • Shard the input into smaller reads using a BigQuery query.

    Why it's wrong here

    Sharding might help but it changes the pipeline logic and may not fix the memory issue if the transform is memory-intensive.

  • Increase the disk size per worker.

    Why it's wrong here

    Disk size is for storage, not memory; OOM is a memory issue.

  • Enable Dataflow Prime with vertical autoscaling.

    Why it's wrong here

    Vertical autoscaling adjusts memory within the same machine family, but may not increase memory enough if the default type is already too low.

Visual reference

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