Explains the enterprise data model and its subject areas
Subject areas named after business concepts, each with one owning domain, holding entities that cite glossary terms (DMBOK2 ch. 4).
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Subject areas named after business concepts, each with one owning domain, holding entities that cite glossary terms (DMBOK2 ch. 4).
How it is assessed: Answers a written question on the enterprise data model correctly
Related knobits
Needed for
- A-201 Recognises overlapping subject areas
- A-202 Recognises an entity in two subject areas
- A-205 Recognises an application-named subject area
- A-206 Recognises an enterprise term with no entity
- A-207 Recognises a subject area with no owning domain
- A-210 Recognises an enterprise entity with no glossary term
- A-231 Redistributes an application-named subject area
All 17 knobits of Build the enterprise data model of subject areas.
Source: Practice the stage 2 reference and review checklist rest on