Exam Professional Machine Learning Engineer topic 1 question 64 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 64
Topic #: 1

[All Professional Machine Learning Engineer Questions]

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?

A. Use a built-in model available on AI Platform Training.
B. Build your custom container to run jobs on AI Platform Training.
C. Build your custom containers to run distributed training jobs on AI Platform Training.
D. Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

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Suggested Answer: C 🗳️

by Vedjha at Dec. 7, 2022, 11:46 p.m.

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mil_spyro

Highly Voted 2 years ago

Selected Answer: C

Answer C. 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 Vertex AI. Model and your data are too large to fit in memory on a single machine hence distributed training jobs. https://cloud.google.com/vertex-ai/docs/training/containers-overview

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