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Google Professional Machine Learning Engineer Exam - Topic 1 Question 61 Discussion

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?
A) Dataplex. Vertex Al Feature Store and Vertex Al TensorBoard
B) Vertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments
C) Dataplex. Vertex Al Experiments, and Vertex Al ML Metadata
D) Vertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata

Google Professional Machine Learning Engineer Exam - 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?

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Suggested Answer: A

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

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Luther
9 months ago
Wait, can you really compare versions across different projects like that?
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Vernell
9 months ago
I agree, Vertex AI Feature Store is essential for model performance.
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Tyisha
9 months ago
Not sure about Dataplex being necessary here.
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Vi
9 months ago
I think Vertex AI Experiments is a must too!
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Evan
9 months ago
Definitely need Vertex AI Pipelines for tracking versions.
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Cheryl
9 months ago
I believe Vertex AI TensorBoard is essential for visualizing model performance, but I can't recall if it's in the right option. Maybe A?
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Bong
9 months ago
I’m a bit confused about whether Dataplex is necessary here. I feel like it’s more about data management than model performance tracking.
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Tandra
9 months ago
I remember practicing a question that involved Vertex AI Pipelines and how they help in managing workflows, so I’m leaning towards option B.
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Nichelle
9 months ago
I think we need to track model versions, so Vertex AI Experiments might be crucial, but I'm not sure about the other services.
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Stefan
9 months ago
I vaguely remember that Vertex AI TensorBoard is useful for visualizing metrics, but I don't see it in any of the options here. Am I missing something?
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Katina
9 months ago
I practiced a similar question, and I feel like Vertex AI Feature Store is essential for managing features across projects, but I'm torn between options B and D.
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Rosenda
9 months ago
I think Vertex AI Experiments is definitely important for tracking model performance, but I can't recall if it pairs best with Pipelines or Metadata.
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Tiffiny
10 months ago
I remember we discussed Vertex AI Pipelines for managing workflows, but I'm not sure if that's the best choice here.
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Abel
10 months ago
I'm leaning towards option D, moving the hardware to a secure location. That way, you're physically securing the system, which could be more effective than just relying on software-based security measures. Of course, you'd still need to consider the other options as well, but this one seems like a good starting point.
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Filiberto
10 months ago
I'm a little confused by this question. I don't recall learning about all these specific string display options. Maybe I should review my notes to make sure I understand the material before answering.
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Idella
1 year 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 year 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 year 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
1 year ago
User 3: Yeah, I think I'll go with option A as well. Thanks for the input!
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Sheridan
1 year ago
User 2: I agree. Vertex AI Feature Store and TensorBoard should be enough for monitoring.
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King
1 year ago
User 1: I think option A is the best choice. Dataplex might not be necessary.
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Rosenda
1 year 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
1 year ago
Option D seems like the best choice for tracking and comparing model performance across different versions.
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Salley
1 year ago
Metadata can definitely provide valuable insights into the performance of your models.
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Blair
1 year ago
I agree, having visibility into your models is crucial for effective management.
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Hailey
1 year 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
1 year ago
It's great to have a solution that can handle model versions across different Google Cloud Projects.
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Hyun
1 year ago
Using those services together would definitely streamline the ML workflow and improve efficiency.
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Laura
1 year 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
1 year ago
I agree, option B seems like the most comprehensive choice for tracking and comparing model performance.
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Muriel
1 year ago
I've used Vertex AI Pipelines before and it really streamlines the process of training and deploying models.
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Celestine
1 year ago
Yeah, those services would definitely help in managing and comparing different versions of models effectively.
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Rosio
1 year 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 year ago
I agree, option B seems like the most comprehensive solution for managing models across multiple projects.
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Izetta
1 year ago
I'm not sure about Dataplex. Should we include it as well?
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Cecil
1 year ago
I agree with Justine. Those services will help us track and compare the performance of our models effectively.
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Justine
1 year ago
I think we should include Vertex AI Pipelines, Feature Store, and Experiments in our ML workflow.
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