Chunking, Extraction, Delta and Unity Catalog
Data preparation requires extracting source content, chunking documents, using Delta tables, and governing source assets through Unity Catalog.
How to study for the Databricks Generative AI Engineer Associate exam
Treat each item as a production GenAI decision: define the task, prepare governed data, build the prompt or agent, package deployment, then evaluate, monitor, and control risk.
Core concepts
Concept 1
Chunking should preserve semantic boundaries, metadata, source references, and enough context for the retrieval task.
Exam cue: Chunk by meaning and retrieval need instead of using only a fixed character count.
Concept 2
Delta tables provide reliable lakehouse storage for extracted documents, parsed text, embeddings metadata, and pipeline outputs.
Exam cue: Store source metadata so answers can cite, filter, and audit retrieved content.
Concept 3
Unity Catalog governs data, features, models, volumes, functions, permissions, lineage, and access policies used by generative AI applications.
Exam cue: Use Unity Catalog to govern who can read source data or call functions.
Risk pitfalls and guardrails
Creating chunks that lose table headers, section labels, or source metadata.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Indexing content that the target user is not allowed to access.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Treating extracted text as unmanaged files outside lineage and permission controls.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Memory anchors
Semantic Chunk
A semantic chunk keeps related text together so retrieval returns useful context instead of fragments.
Chunk Metadata
Chunk metadata records source, owner, timestamp, permissions, page, section, and business attributes.
Delta Table
A Delta table stores versioned lakehouse data used by extraction, indexing, and retrieval pipelines.
Unity Catalog
Unity Catalog centralizes governance for data, models, functions, lineage, permissions, and access policies.
Source Lineage
Source lineage shows where content came from and how it moved through extraction and indexing.
Permission Filter
A permission filter prevents retrieval from returning content the requesting user cannot access.
Extraction Pipeline
An extraction pipeline parses documents, normalizes text, attaches metadata, and writes governed outputs.
Context Preservation
Context preservation keeps titles, headings, tables, and neighboring text available for answer grounding.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A RAG source consists of scanned PNG pages with no embedded text. Which Python package is the most direct choice for extracting text?
An ingestion job must preserve paragraphs, headings, and tables from DOCX policy files. Which package is the best fit?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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