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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #75

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on…

The correct answer is C. Build your custom containers to run distributed training jobs on AI Platform Training. By running your machine learning (ML) training job in a custom container, you can use ML frameworks, non-ML dependencies, libraries, and binaries that are not otherwise supported on Model and your data are too large to fit in memory on a single machine hence distributed…

Submitted by ahmad_uae· Apr 18, 2026ML pipeline operationalization

Question

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

Options

  • AUse a built-in model available on AI Platform Training.
  • BBuild your custom container to run jobs on AI Platform Training.
  • CBuild your custom containers to run distributed training jobs on AI Platform Training.
  • DReconfigure your code to a ML framework with dependencies that are supported by AI Platform

How the community answered

(23 responses)
  • A
    4% (1)
  • B
    4% (1)
  • C
    83% (19)
  • D
    9% (2)

Explanation

By running your machine learning (ML) training job in a custom container, you can use ML frameworks, non-ML dependencies, libraries, and binaries that are not otherwise supported on Model and your data are too large to fit in memory on a single machine hence distributed training https://cloud.google.com/vertex-ai/docs/training/containers-overview

Topics

#AI Platform Training#Custom containers#Distributed training#Large-scale models

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