Google Exam Professional Machine Learning Engineer Topic 1 Question 61 Discussion

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

Question #: 61
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?

ADataplex. Vertex Al Feature Store and Vertex Al TensorBoard

BVertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments

CDataplex. Vertex Al Experiments, and Vertex Al ML Metadata

DVertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata

Show Suggested Answer

Suggested Answer: A

by Elly at Mar 21, 2023, 07:53 PM

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Idella

1 months ago

Hey, if I can't track my models, can I at least track my steps with a Vertex AI Fitbit?

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Bernardine

1 months ago

Option C is intriguing, but Vertex AI Experiments and ML Metadata alone might not be enough. I'd feel more comfortable with a full-fledged pipeline solution.

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Cassi

1 months ago

Option A looks promising, but I'm not sure Dataplex is really necessary here. Vertex AI Feature Store and TensorBoard should give me the tools I need to monitor my models.

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Han

6 days ago

User 3: Yeah, I think I'll go with option A as well. Thanks for the input!

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Sheridan

11 days ago

User 2: I agree. Vertex AI Feature Store and TensorBoard should be enough for monitoring.

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King

14 days ago

User 1: I think option A is the best choice. Dataplex might not be necessary.

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Rosenda

1 months ago

I'm leaning towards option D. Vertex AI Pipelines, Experiments, and Metadata sound like they'd give me the visibility I need to manage my models effectively. Plus, who doesn't love a bit of metadata?

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Chaya

6 days ago

Option D seems like the best choice for tracking and comparing model performance across different versions.

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Salley

15 days ago

Metadata can definitely provide valuable insights into the performance of your models.

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Blair

1 months ago

I agree, having visibility into your models is crucial for effective management.

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Hailey

2 months ago

Hmm, I think option B is the way to go. Vertex AI Pipelines, Feature Store, and Experiments seem like a comprehensive solution for tracking and comparing model performance across multiple projects.

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Kirk

5 days ago

It's great to have a solution that can handle model versions across different Google Cloud Projects.

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Hyun

6 days ago

Using those services together would definitely streamline the ML workflow and improve efficiency.

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Laura

7 days ago

I think Vertex AI Pipelines, Feature Store, and Experiments would make it easier to manage models across multiple projects.

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Delsie

10 days ago

I agree, option B seems like the most comprehensive choice for tracking and comparing model performance.

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Muriel

12 days ago

I've used Vertex AI Pipelines before and it really streamlines the process of training and deploying models.

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Celestine

14 days ago

Yeah, those services would definitely help in managing and comparing different versions of models effectively.

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Rosio

1 months ago

I think Vertex AI Pipelines, Feature Store, and Experiments would provide a good workflow for tracking model performance.

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Helga

1 months ago

I agree, option B seems like the most comprehensive solution for managing models across multiple projects.

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Izetta

2 months ago

I'm not sure about Dataplex. Should we include it as well?

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Cecil

2 months ago

I agree with Justine. Those services will help us track and compare the performance of our models effectively.

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Justine

3 months ago

I think we should include Vertex AI Pipelines, Feature Store, and Experiments in our ML workflow.

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