Databricks GENAI-ASSOC Data Preparation Guide

Study Databricks GENAI-ASSOC Data Preparation: key concepts, common traps, and exam decision cues.

This chapter is where reliable GenAI systems usually succeed or fail. Databricks expects you to know how source quality, extraction, chunking, table design, and retrieval evaluation shape the whole application.

Work this domain in order

Lesson	Focus
2.1 Sources & Extraction	Learn how document quality and extraction choices affect downstream system behavior.
2.2 Chunking & Retrieval Inputs	Learn how chunking, metadata, and Delta tables in Unity Catalog set up good retrieval.
2.3 Reranking & Retrieval Quality	Learn how Databricks tests retrieval metrics, reranking, and advanced chunking patterns.

Fast routing inside this chapter

If the question is really about…	Go first to…
source quality or extraction package fit	2.1 Sources & Extraction
chunking, metadata, or writing retrieval inputs to Delta tables	2.2 Chunking & Retrieval Inputs
reranking, retrieval metrics, or advanced chunking strategy	2.3 Reranking & Retrieval Quality

What strong answers usually do

repair source and extraction issues before blaming the prompt
choose chunking based on documents, model constraints, and retrieval goals
separate data-preparation quality from evaluation and deployment concerns

In this section

Databricks GENAI-ASSOC Source Extraction and Cleaning Guide
Study Databricks GENAI-ASSOC Source Extraction and Cleaning: key concepts, common traps, and exam decision cues.
Databricks GENAI-ASSOC Chunking and Retrieval Inputs Guide
Study Databricks GENAI-ASSOC Chunking and Retrieval Inputs: key concepts, common traps, and exam decision cues.
Databricks GENAI-ASSOC Retrieval Quality and Reranking Guide
Study Databricks GENAI-ASSOC Retrieval Quality and Reranking: key concepts, common traps, and exam decision cues.

Revised on Sunday, May 10, 2026

1. Design

3. Development