Google Exam Professional-Machine-Learning-Engineer Topic 5 Question 75 Discussion

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

Question #: 75
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.

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Suggested Answer: D

by Mary at Apr 24, 2024, 07:30 PM

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5 days ago

That sounds like a good option for tracking the models and datasets.

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Mabel

19 hours ago

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

upvoted 0 times

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Marica

6 days ago

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

upvoted 0 times

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