Google Exam Professional Machine Learning Engineer Topic 3 Question 99 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 99
Topic #: 3

[All Professional Machine Learning Engineer Questions]

You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give Reference and Explanation)

AConfigure a Compute Engine VM with all the dependencies that launches the training Train your model with Vertex Al using a custom tier that contains the required GPUs.

BPackage your code with Setuptools. and use a pre-built container Train your model with Vertex Al using a custom tier that contains the required GPUs.

CCreate a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model

DCreate a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

Show Suggested Answer

Suggested Answer: C

Traffic splitting is a feature of Vertex AI that allows you to distribute the prediction requests among multiple models or model versions within the same endpoint. You can specify the percentage of traffic that each model or model version receives, and change it at any time. Traffic splitting can help you test the new model in production without creating a new endpoint or a separate service. You can deploy the new model to the existing Vertex AI endpoint, and use traffic splitting to send 5% of production traffic to the new model. You can monitor the end-user metrics, such as listening time, to compare the performance of the new model and the previous model. If the end-user metrics improve between models over time, you can gradually increase the percentage of production traffic sent to the new model. This solution can help you test the new model in production while minimizing complexity and cost.Reference:

Traffic splitting | Vertex AI

Deploying models to endpoints | Vertex AI

by Gearldine at Mar 19, 2025, 02:08 PM

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Lauran

5 days ago

Option A seems like the easiest way to scale my training workload. I can simply configure a Compute Engine VM with the necessary dependencies and use Vertex AI to train my model. Definitely the fastest and most cost-effective solution.

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Jesusita

5 days ago

I'm not sure, but I think option D could also be a valid choice. Creating a GKE cluster with a node pool that has 4 V100 GPUs might be a good solution too.

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Paul

10 days ago

I agree with Robt. Option A seems like the most efficient way to scale the training workload while minimizing cost.

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Robt

13 days ago

I think the correct answer is A. It involves configuring a Compute Engine VM with the necessary dependencies and using Vertex AI with the required GPUs.

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