Question
A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?
- A. Model Correct
- B. Component
- C. Environment
- D. Pipeline
Explanation
The artifact an experiment produces is the trained model, and registering it as a model asset stores and versions it in the model registry, keeps lineage back to the job that trained it, and lets it be shared through a registry and reused in other projects. A component packages a reusable pipeline step (code, command and interface); it is something you author to run experiments, not the artifact experimentation creates. An environment only defines the runtime dependencies, and a pipeline is a workflow definition rather than a registered output.
