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Google Exam Professional Machine Learning Engineer Topic 5 Question 75 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 75
Topic #: 5
[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 Mary at Apr 24, 2024, 07:30 PM

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Dorothy
5 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
5 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
5 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
7 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
7 months ago
Agreed, it seems to provide the level of detail needed for compliance tracking.
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Marica
7 months ago
I think using the Vertex AI Metadata API might be the most effective method.
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Major
7 months ago
That could simplify the process of tracking which model was used for predictions.
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Chau
7 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
7 months ago
That seems like a comprehensive way to ensure compliance requirements are met.
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Major
7 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
7 months ago
That could work too, but it might not provide as much detailed tracking.
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Rose
7 months ago
B) Create a Vertex AI experiment, and enable autologging inside the custom job.
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Karma
7 months ago
That sounds like a good option for tracking the models and datasets.
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Carrol
7 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
7 months ago
A) Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
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Marica
7 months ago
A) Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
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