Exam Professional Machine Learning Engineer topic 1 question 5 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 5
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

A. Use the AI Platform custom containers feature to receive training jobs using any framework.
B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Show Suggested Answer

Suggested Answer: A 🗳️

by gcp2021go at June 5, 2021, 5:40 p.m.

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gcp2021go

Highly Voted 4 years ago

the answer is A

upvoted 25 times

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guruguru

Highly Voted 3 years, 10 months ago

A, because AI platform supported all the frameworks mentioned. And Kubeflow is not managed service in GCP. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch

upvoted 10 times

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jkkim_jt

Most Recent 8 months ago

I think "custom container" should be written in capital letters like "AI Platform Custom Container". It is one of the features of AI Platform. It is a proper noun not a general term.

upvoted 3 times

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Yorko

11 months, 2 weeks ago

Selected Answer: A

A. Now it's called Vertex AI

upvoted 4 times

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PhilipKoku

1 year ago

Selected Answer: A

The best approach is option A: Use AI Platform custom containers. It provides flexibility, scalability, and support for various frameworks, making it an ideal choice for your team’s needs.

upvoted 1 times

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