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Answer: 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.
The correct answer is A: 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. This approach leverages the Vertex AI Metadata API, which provides low-level primitives for creating custom metadata entities and relationships (contexts, executions, artifacts, and events). This allows for detailed tracking of which model was used for a specific prediction and access to all related artifacts. This is particularly important for compliance and accountability. Options B, C, and D do not offer the same level of granularity and flexibility for custom metadata tracking.
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You have a custom job that runs on Vertex AI on a weekly basis. This job is part of a proprietary ML workflow that generates datasets, models, and custom artifacts, and stores them in a Cloud Storage bucket. Over time, many versions of datasets and models are created. Due to compliance requirements, your company must be able to track which model was used to make a specific prediction and have access to all related artifacts for each model. How should you configure your workflows to meet these compliance and tracking requirements?
A
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.
B
Create a Vertex AI experiment, and enable autologging inside the custom job.
C
Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
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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