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.

Show Suggested Answer

Suggested Answer: D

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

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Dorothy

4 months ago

That's true, option C could also work well for this scenario. It's important to consider all the options before making a decision.

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Vanda

4 months ago

I'm not sure, option C also sounds like a viable solution. Creating context, execution, and artifacts for each model might provide a clear overview of the workflow.

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Andree

4 months ago

I agree, option A seems like the most efficient way to meet the compliance requirements. It's important to have a structured database to keep track of all the models.

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Dorothy

5 months ago

I think option A sounds like the best choice for tracking the models used for predictions. Using a ML Metadata database would make it easy to keep everything organized.

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Rose

5 months ago

Agreed, it seems to provide the level of detail needed for compliance tracking.

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Marica

6 months ago

I think using the Vertex AI Metadata API might be the most effective method.

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Major

6 months ago

That could simplify the process of tracking which model was used for predictions.

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Chau

6 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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Karma

6 months ago

That seems like a comprehensive way to ensure compliance requirements are met.

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Major

6 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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Marica

6 months ago

That could work too, but it might not provide as much detailed tracking.

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Rose

6 months ago

B) Create a Vertex AI experiment, and enable autologging inside the custom job.

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Karma

6 months ago

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

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Carrol

6 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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Mabel

6 months ago

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

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Marica

6 months ago

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

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