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Google Professional Machine Learning Engineer Exam - Topic 4 Question 73 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 73
Topic #: 4
[All Professional Machine Learning Engineer Questions]

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

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Suggested Answer: D

by Tammara at Mar 19, 2024, 03:08 PM

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Erinn
3 months ago
I disagree, B might not provide enough detail for compliance needs.
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Octavio
3 months ago
Wait, can you really track all that with just the Metadata API?
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Gladys
4 months ago
A seems a bit overkill for just tracking models, right?
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Keena
4 months ago
I think D is a solid choice too, especially for organization.
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Franchesca
4 months ago
Option C sounds like the best fit for tracking everything.
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Jose
4 months ago
I feel like registering models in the Vertex AI Model Registry makes sense for tracking, but I wonder if it’s enough for our compliance requirements.
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Levi
4 months ago
The Vertex AI Metadata API sounds familiar, especially for linking models and artifacts, but I’m a bit confused about how to implement it correctly.
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Mozell
5 months ago
I think enabling autologging in Vertex AI experiments could help, but I can't recall if it covers all the compliance needs we studied.
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Kindra
5 months ago
I remember we discussed the importance of tracking models and datasets for compliance, but I'm not sure if TFX is the best option here.
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Gertude
5 months ago
I'm a bit unsure about this one. There are a few different options presented, and I'm not sure which one would be the most appropriate. I think I'll start by reviewing the details of each approach and try to determine the pros and cons before making a decision.
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Trina
5 months ago
This is a great question that really tests our understanding of Vertex AI and model management. I feel pretty confident that the Vertex AI Metadata API is the way to go here. It allows us to create the necessary context, execution, and artifact tracking to satisfy the compliance requirements.
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Mignon
5 months ago
Okay, I think I have a strategy for this. Based on the compliance requirements, it sounds like we need a robust way to track the model lineage and associated artifacts. The Vertex AI Metadata API seems like the most comprehensive option to meet those needs, so I'll focus on that approach.
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Romana
5 months ago
Hmm, I'm a bit confused by all the different options here. I know we need to track the model versions and artifacts, but I'm not sure which approach would be the best fit for our requirements. I'll have to read through the question carefully and think it through.
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Deonna
5 months ago
This seems like a tricky one. I'm not too familiar with Vertex AI and the different options for tracking model provenance, but I think I'll try to break it down step-by-step.
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Myra
5 months ago
Easy peasy, the answer is A. tmp_name is the key you want for the provisional file name.
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Tamar
5 months ago
This looks straightforward. The Scrum Guide is the foundation, so multiple teams must still follow it even when scaling.
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Lauran
2 years ago
I personally think option C) Use the Vertex AI Metadata API inside the custom Job is the most efficient solution.
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Gwenn
2 years ago
I disagree, I believe option D) Register each model in Vertex AI Model Registry is the way to go.
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Brett
2 years ago
I think the best option is A) Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
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Naomi
2 years ago
I'm not sure, but option C sounds good to me. Using Vertex AI Metadata API inside the job seems like a practical approach.
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Thomasena
2 years ago
I agree with Georgeanna. It's important to have a centralized database to keep track of all the models and datasets.
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Georgeanna
2 years ago
I think option A is the best choice. By using TFX ML Metadata database, we can easily track the model used for predictions.
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Twana
2 years ago
Hmm, I'm not sure about option B. Relying on autologging in Vertex AI may not give us enough control over the metadata. And a separate TFX metadata database (option A) sounds like overkill for this use case.
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Eladia
2 years ago
Option D also sounds promising - registering the models in the Vertex AI Model Registry and using labels could be a simple way to manage the versioning and provenance. But I'm not sure how robust that would be for a complex workflow.
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Honey
2 years ago
I'm leaning towards option C. Using the Vertex AI Metadata API seems like the most direct way to link the models, datasets, and artifacts together. Plus, we can create custom context and execution details to meet the compliance needs.
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Emerson
2 years ago
I'm convinced, option C it is!
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Shawnee
2 years ago
It's definitely a more structured way to track model usage and artifacts.
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Brande
2 years ago
Using events to link everything sounds like a good approach.
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Alona
2 years ago
That makes sense, it's important to link all the necessary information together.
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Kristeen
2 years ago
We can create custom context and execution details as needed for compliance.
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Norah
2 years ago
Agreed, using the Vertex AI Metadata API seems like the most direct solution.
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Truman
2 years ago
I think option C is the best choice.
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Chaya
2 years ago
Whoa, this question looks like a real brain-teaser! We definitely need to track the models and artifacts for compliance, but it's not clear which option is the best approach.
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