Chapter 5 — Data Modeling and Design
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Fundamentals weight: 11% — major domain
This chapter is rebuilt from the current Chapter 5 Mastery Lab package in Google Drive, not from the superseded GitHub chapter page. The source boundary is DAMA-DMBOK2 Revised, Chapter 5 — Data Modeling and Design. DMBOK terminology and distinctions control.
The chapter in one sentence
Data Modeling and Design turns business/data requirements into progressively more precise representations — from conceptual meaning, through logical structure, to physical implementation — while preserving definitions, relationship rules, valid values, traceability, review, and change control.
Mastery loop
LEARN → RECALL → COMPARE → APPLY → TEST → DIAGNOSE → REVISIT
- Guided Learning Guide
- Exam Map & High-Yield Targets
- Visual Memory Map / Framework Atlas
- Comparison & Battle Cards
- Scenario Lab
- Chapter Question Bank
- Teach-Back & Blank-Page Recall
Artifact 08 remains performance-driven. It should be created only after real question-bank and review errors exist.
What makes Chapter 5 hard
The domain contains many terms that look almost interchangeable until the deciding clue is isolated: entity vs instance; cardinality vs arity; composite vs compound; super vs candidate; primary vs alternate; business vs surrogate; identifying vs non-identifying; relational vs dimensional; fact vs dimension; grain vs cardinality; CDM vs LDM vs PDM; view vs materialized view; vertical vs horizontal partition; normalization vs denormalization; generalization vs specialization; forward vs reverse engineering; pattern vs industry model.
The exam skill is therefore not merely remembering definitions. It is recognizing which feature changes the classification.