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Exam Professional Machine Learning Engineer All Questions

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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 🗳️

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mil_spyro
Highly Voted 1 year, 10 months 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
upvoted 11 times
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PhilipKoku
Most Recent 4 months, 3 weeks ago
Selected Answer: C
C) Distributed training with customer containers
upvoted 1 times
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MultiCloudIronMan
7 months ago
Selected Answer: C
This allows using external dependences and distributed training will solve the memory issues
upvoted 3 times
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Werner123
8 months ago
Selected Answer: C
Critical dependencies that are not supported -> Custom container Too large to fit in memory on a single machine -> Distributed
upvoted 2 times
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M25
1 year, 5 months ago
Selected Answer: C
Went with C
upvoted 1 times
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wish0035
1 year, 10 months ago
Selected Answer: C
ans: C A, D => too much work. B => discarded because "model and your data are too large to fit in memory on a single machine"
upvoted 1 times
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ares81
1 year, 10 months ago
C, for me!
upvoted 1 times
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JeanEl
1 year, 10 months ago
Selected Answer: C
I think it's C
upvoted 1 times
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Vedjha
1 year, 10 months ago
Will go for 'C'- Custom containers can address the env limitation and distributed processing will handle the data volume
upvoted 1 times
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C (25%)
B (20%)
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