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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Norah

2 months ago

Wait, I can use my refrigerator as a GPU? *scratches head* Nah, that's probably not a good idea. Time to get serious and go with Option B. Gotta package that code up and let Vertex AI handle the heavy lifting!

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Hyun

23 days ago

That's right, Option B is the best choice for scaling your training workload efficiently.

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Nicholle

1 months ago

Option B sounds like the way to go. Package your code and let Vertex AI do the heavy lifting.

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Ronny

1 months ago

Yeah, using your refrigerator as a GPU is definitely not a good idea.

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Rhea

2 months ago

Hmm, I'm not sure I trust any of these options. I think I'll just train my model on my local laptop and hope it scales eventually. Who needs fancy cloud infrastructure anyway? *laughs nervously*

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Carmelina

20 days ago

Training on your local laptop may work for small datasets, but for 200k labeled images, utilizing cloud resources like V100 GPUs will definitely improve your model's performance and efficiency.

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Eva

29 days ago

Using cloud infrastructure like Vertex AI can greatly speed up your training process and save you time in the long run. It's worth considering!

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Jessenia

2 months ago

Option A seems like the best choice for scaling your training workload with minimal cost. You should configure a Compute Engine VM with the necessary dependencies and use Vertex AI with custom tier containing the required GPUs.

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Lashandra

2 months ago

Option D is intriguing, but it sounds a bit more complex than I'd like to deal with. Setting up a GKE cluster and submitting a TFJob operator seems like a lot of work. I'm more interested in a simpler, managed solution like Vertex AI.

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Ernestine

2 months ago

I'm leaning towards Option C. Creating a Vertex AI Workbench instance with the required GPUs sounds like a great way to get my model training up and running quickly. Plus, I don't have to worry about managing the infrastructure myself.

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Theodora

9 hours ago

Absolutely, Vertex AI Workbench provides a hassle-free way to scale your training workload efficiently.

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Melvin

13 days ago

It's definitely a convenient option. Plus, you can focus more on optimizing your model rather than setting up the environment.

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Bev

16 days ago

I agree, managing the infrastructure myself can be time-consuming. Vertex AI Workbench takes care of that.

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Asha

1 months ago

Option C seems like a good choice. Using Vertex AI Workbench with 4 V100 GPUs can speed up training.

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Lauran

2 months 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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Layla

29 days ago

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Arlette

1 months ago

A) Configure a Compute Engine VM with all the dependencies that launches the training Train your model with Vertex AI using a custom tier that contains the required GPUs.

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Jesusita

2 months 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

3 months 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

3 months 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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