PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #295
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #295: Real Exam Question with Answer & Explanation
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Question
You have a custom job that runs on Vertex AI 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 requirements?
Options
- AUse the Vertex AI Metadata API inside the custom job to create context, execution, and artifacts
- BCreate a Vertex AI experiment, and enable autologging inside the custom job.
- CConfigure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
- DRegister each model in Vertex AI Model Registry, and use model labels to store the related
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