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?

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.