Google Exam Professional-Machine-Learning-Engineer Topic 5 Question 80 Discussion

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

Question #: 80
Topic #: 5

[All Google 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?

AConfigure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

BCreate a Vertex Al experiment, and enable autologging inside the custom job

CUse the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

DRegister each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Show Suggested Answer

Suggested Answer: D

by Chauncey at Jun 21, 2024, 09:30 AM

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Val

3 months ago

I'm leaning towards option C. Using the Vertex AI Metadata API inside the custom job to create context, execution, and artifacts for each model seems like a comprehensive approach.

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Felix

3 months ago

Option C is the way to go. Keeping everything tied together in the Vertex AI Metadata API is the most elegant solution. Plus, it'll make the auditors happy!

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Hector

2 months ago

Yeah, it's important to have a clear record of which model was used for each prediction.

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Katina

2 months ago

I agree, using the Vertex AI Metadata API will definitely help keep everything organized.

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Frederica

3 months ago

Haha, this is a tricky one! I bet the engineers who designed this system had a headache trying to figure out the best way to handle all the versioning and compliance needs.

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Erasmo

2 months ago

D) Register each model in Vertex AI Model Registry, and use model labels to store the related dataset and model information.

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Armanda

2 months ago

C) Use the Vertex AI Metadata API inside the custom job to create context, execution, and artifacts for each model, and use events to link them together.

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Arlyne

3 months ago

A) Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

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Chun

3 months ago

I disagree, I believe option D is the way to go. Registering each model in Vertex AI Model Registry will make it easier to store the related information.

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Louann

3 months ago

I think we should go with option A, configuring a TFX ML Metadata database seems like the best way to track the models.

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Alisha

3 months ago

I was leaning towards Option D, but after thinking it through, Option C provides a more comprehensive solution to manage the full lineage of the models and datasets.

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Shawnta

2 months ago

I agree, Option C seems like a better solution for tracking the full lineage of the models and datasets.

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Latia

3 months ago

Option D is a good choice for organizing the models.

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Marisha

4 months ago

Option C seems like the best choice here. Tracking the metadata and linking the artifacts directly in the Vertex AI Metadata API is the most robust approach to meet the compliance requirements.

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Iola

3 months ago

I also believe that option C is the best choice. It allows for easy tracking of models and datasets, making it easier to meet the compliance needs.

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Jannette

3 months ago

Agreed, option C provides a comprehensive way to manage the datasets, models, and artifacts while ensuring compliance with the requirements.

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Vanda

4 months ago

I think option C is the way to go. Using the Vertex AI Metadata API to link the artifacts and track the metadata seems like the most efficient solution.

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