Databricks ML-ASSOC Model Deployment Guide

Study Databricks ML-ASSOC Model Deployment: key concepts, common traps, and exam decision cues.

This chapter covers how a trained model becomes a usable Databricks service or pipeline component. The exam wants you to know when batch, realtime, and streaming inference each fit and what must stay consistent from training to serving.

Work this domain in order

Lesson	Focus
4.1 Batch, Realtime and Streaming Serving Patterns	Learn the high-level serving patterns Databricks tests in deployment scenarios.
4.2 Custom Endpoints, Traffic Splits and Inference Consistency	Learn how custom endpoints, query behavior, and traffic splits fit controlled serving.

Fast routing inside this chapter

If the question is really about…	Go first to…
batch, realtime, or streaming inference choice	4.1 Batch, Realtime and Streaming Serving Patterns
custom endpoints, querying served models, or traffic splits	4.2 Custom Endpoints, Traffic Splits and Inference Consistency

What strong answers usually do

match the serving pattern to the workload shape
preserve preprocessing and schema consistency from training to serving
separate endpoint behavior from offline evaluation behavior

In this section

Databricks ML-ASSOC Serving Patterns Guide
Study Databricks ML-ASSOC Serving Patterns: key concepts, common traps, and exam decision cues.
Databricks ML-ASSOC Custom Endpoints and Traffic Splits Guide
Study Databricks ML-ASSOC Custom Endpoints and Traffic Splits: key concepts, common traps, and exam decision cues.

Revised on Sunday, May 10, 2026

3. Model Development

Study Plan