Certified Machine Learning ProfessionalDemo

Question

A Machine Learning Engineer must deploy a production ML workflow that includes an MLflow experiment to track model-training runs, a registered model in Unity Catalog for version management, and a model serving endpoint for real-time inference. The team needs a unified configuration approach that provides consistent deployment across development and production environments while following infrastructure-as-code best practices.

Which approach should the Machine Learning Engineer use to define all three components together?

A Use MLflow’s deployment tools to create individual deployment configurations for each component, then orchestrate them using Databricks Jobs.
B Use Terraform to provision the infrastructure and then manually configure each ML component through the Databricks UI.
C Define experiments, registered_models, and model_serving_endpoints resources in a Databricks Asset Bundle (DAB) configuration file.
D Create separate REST API calls for each component and run them sequentially using a shell script.

Explanation

Databricks Asset Bundles use declarative configuration and deployment targets to manage environment-specific Databricks resources as infrastructure as code. A bundle can define MLflow experiments, Unity Catalog registered_models, and model_serving_endpoints together, enabling consistent development and production deployments.

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