Explains the business drivers of data architecture
Strategic preparation, efficiency and the translation of business needs into data and system requirements.
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 67% | Not shown |
| gemini-3.5-flash-lite | Low cost | 47% | Not shown |
| gpt-6-luna | Low cost | 67% | Not shown |
| claude-haiku-5-5 | Low cost | 67% | Not shown |
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: Strategic preparation, efficiency and the translation of business needs into data and system requirements.
How it is assessed: Answers a CDMP-style multiple-choice question on the business drivers of data architecture
All knobits of Data architecture.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 4: Data Architecture, element AV-1