Databricks ML-ASSOC Machine Learning Guide

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.

Work this domain in order

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.

Fast routing inside this chapter

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

What strong answers usually do

  • classify platform feature, experiment tracking, feature reuse, and model-management questions separately
  • choose the Databricks-native workflow that preserves reproducibility
  • keep feature workflow and registry workflow from blurring together

In this section

Revised on Sunday, May 10, 2026