Study Databricks ML-PRO MLOps: key concepts, common traps, and exam decision cues.
This is the other heavy ML-PRO domain. Databricks is testing whether you can turn model-development work into a repeatable, monitored, governed production system.
| Lesson | Focus |
|---|---|
| 2.1 Lifecycle Architecture, Aliases and Deploy Code Strategy | Learn how Databricks maps features to the model lifecycle and release control. |
| 2.2 Unit Tests, Integration Tests and Environment Stages | Learn how ML-PRO frames testing scope across ML systems. |
| 2.3 Environment Architecture and Asset Bundles for ML Assets | Learn how Databricks expects you to structure ML environments and deployed assets. |
| 2.4 Automated Retraining and Model Selection Strategy | Learn how retraining workflows should decide when and how to promote a new model. |
| 2.5 Lakehouse Monitoring, Drift Metrics and Alerting Design | Learn how monitoring, drift detection, and alerting fit into ML-PRO. |
| If the question is really about… | Go first to… |
|---|---|
| aliases, lifecycle steps, or deploy-code strategy | 2.1 Lifecycle Architecture, Aliases and Deploy Code Strategy |
| unit or integration tests and environment stages | 2.2 Unit Tests, Integration Tests and Environment Stages |
| Databricks environments, Asset Bundles, or ML asset deployment | 2.3 Environment Architecture and Asset Bundles for ML Assets |
| automated retraining triggers and top-model selection | 2.4 Automated Retraining and Model Selection Strategy |
| drift metrics, monitoring table types, or alerting thresholds | 2.5 Lakehouse Monitoring, Drift Metrics and Alerting Design |