Google Exam Professional Machine Learning Engineer Topic 6 Question 82 Discussion

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

Question #: 82
Topic #: 6

[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?

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 Della at Jul 11, 2024, 12:48 AM

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Franklyn

4 months ago

Option B seems like the easy way out, but I don't think it fully addresses the compliance requirements. I'd go with Option C for the win.

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Doyle

4 months ago

Hold up, are we sure we can't just use a spreadsheet for this? I kid, I kid. Option C seems like the right call - gotta love that Vertex AI Metadata API!

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Elvera

3 months ago

Yeah, using the Vertex AI Metadata API inside the custom job sounds like the best way to track everything.

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Justine

4 months ago

Option C seems like the right call - gotta love that Vertex AI Metadata API!

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Freeman

4 months ago

I think using the Vertex AI Metadata API inside the custom job could be a more efficient way to meet the requirements.

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Quentin

4 months ago

This is a tricky one, but I think Option C is the way to go. Manually tracking the model provenance using the Metadata API seems like the most robust solution.

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Brendan

3 months ago

Using the ML Metadata API with Option A might be a good choice too, it's all about what works best for your workflow.

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Cathrine

3 months ago

I think Option D could also work well, registering each model in the Model Registry seems organized.

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Annmarie

3 months ago

I agree, Option C sounds like the most reliable way to track the model provenance.

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Lai

4 months ago

I believe registering each model in Vertex AI Model Registry could also be a good option.

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Celestina

4 months ago

I'm leaning towards Option D. Registering the models in the Vertex AI Model Registry and using labels to store the related information sounds like a more straightforward solution.

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Lynelle

3 months ago

True, but using Vertex AI Model Registry seems more efficient for this scenario.

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Paola

4 months ago

Configuring a TensorFlow Extended (TFX) ML Metadata database could also work for tracking the models.

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Dorthy

4 months ago

I agree, using Vertex AI Model Registry and labels would make it easier to manage.

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Augustine

4 months ago

Option D seems like the best choice for tracking the models and related information.

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Dyan

5 months ago

Option C seems like the best choice here. Using the Vertex AI Metadata API to track the model provenance and link it to the artifacts is a solid approach that meets the compliance requirements.

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Carry

3 months ago

Yes, configuring the workflows to use the Vertex AI Metadata API inside the custom job will provide the necessary context, execution, and artifacts for each model, meeting the compliance requirements.

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Devorah

3 months ago

I agree, Option C sounds like the most efficient way to ensure the company can track which model was used for each prediction.

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Sharmaine

3 months ago

Option C seems like the best choice here. Using the Vertex AI Metadata API to track the model provenance and link it to the artifacts is a solid approach that meets the compliance requirements.

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