Independence Day Deal! Unlock 25% OFF Today – Limited-Time Offer - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

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?

Show Suggested Answer Hide Answer
Suggested Answer: A

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

Limited Time Offer

25%

Off

Get Premium Professional Machine Learning Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel
Idella
1 months ago
Hey, if I can't track my models, can I at least track my steps with a Vertex AI Fitbit?
upvoted 0 times
...
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.
upvoted 0 times
...
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.
upvoted 0 times
Han
5 days ago
User 3: Yeah, I think I'll go with option A as well. Thanks for the input!
upvoted 0 times
...
Sheridan
10 days ago
User 2: I agree. Vertex AI Feature Store and TensorBoard should be enough for monitoring.
upvoted 0 times
...
King
13 days ago
User 1: I think option A is the best choice. Dataplex might not be necessary.
upvoted 0 times
...
...
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?
upvoted 0 times
Chaya
5 days ago
Option D seems like the best choice for tracking and comparing model performance across different versions.
upvoted 0 times
...
Salley
14 days ago
Metadata can definitely provide valuable insights into the performance of your models.
upvoted 0 times
...
Blair
1 months ago
I agree, having visibility into your models is crucial for effective management.
upvoted 0 times
...
...
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.
upvoted 0 times
Kirk
4 days ago
It's great to have a solution that can handle model versions across different Google Cloud Projects.
upvoted 0 times
...
Hyun
5 days ago
Using those services together would definitely streamline the ML workflow and improve efficiency.
upvoted 0 times
...
Laura
6 days ago
I think Vertex AI Pipelines, Feature Store, and Experiments would make it easier to manage models across multiple projects.
upvoted 0 times
...
Delsie
9 days ago
I agree, option B seems like the most comprehensive choice for tracking and comparing model performance.
upvoted 0 times
...
Muriel
11 days ago
I've used Vertex AI Pipelines before and it really streamlines the process of training and deploying models.
upvoted 0 times
...
Celestine
13 days ago
Yeah, those services would definitely help in managing and comparing different versions of models effectively.
upvoted 0 times
...
Rosio
1 months ago
I think Vertex AI Pipelines, Feature Store, and Experiments would provide a good workflow for tracking model performance.
upvoted 0 times
...
Helga
1 months ago
I agree, option B seems like the most comprehensive solution for managing models across multiple projects.
upvoted 0 times
...
...
Izetta
2 months ago
I'm not sure about Dataplex. Should we include it as well?
upvoted 0 times
...
Cecil
2 months ago
I agree with Justine. Those services will help us track and compare the performance of our models effectively.
upvoted 0 times
...
Justine
3 months ago
I think we should include Vertex AI Pipelines, Feature Store, and Experiments in our ML workflow.
upvoted 0 times
...

Save Cancel