Study Databricks ML-ASSOC Machine Learning: key concepts, common traps, and exam decision cues.
This chapter is the platform base of the exam. Databricks is testing whether you understand how its ML workflow fits together before it starts asking about tuning, metrics, or serving.
| Lesson | Focus |
|---|---|
| 1.1 MLOps Strategy, ML Runtimes and AutoML | Learn the Databricks ML platform choices that shape experiment speed and repeatability. |
| 1.2 Feature Store and Unity Catalog Workflows | Learn how feature tables work in Unity Catalog and how they support training and scoring. |
| 1.3 MLflow Runs, Logging and UI Basics | Learn how Databricks expects you to use runs, logging, and the MLflow UI. |
| 1.4 UC Registry, Aliases and Promotion Decisions | Learn how Unity Catalog registry, aliases, and promotion decisions differ from raw experiment tracking. |
| If the question is really about… | Go first to… |
|---|---|
| runtimes, AutoML, or MLOps strategy | 1.1 MLOps Strategy, ML Runtimes and AutoML |
| feature tables and Unity Catalog feature workflow | 1.2 Feature Store and Unity Catalog Workflows |
| MLflow runs, logging, or the UI | 1.3 MLflow Runs, Logging and UI Basics |
| registry, aliases, or promotion choices | 1.4 UC Registry, Aliases and Promotion Decisions |