Explains lifecycle projections
Planning the lifecycle of platforms and standards: current, deployment, strategic, retirement.
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 | 83% | Not shown |
| gemini-3.5-flash-lite | Low cost | 36% | Not shown |
| gpt-6-luna | Low cost | 83% | Not shown |
| claude-haiku-5-5 | Low cost | 83% | 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: Planning the lifecycle of platforms and standards: current, deployment, strategic, retirement.
How it is assessed: Answers a CDMP-style multiple-choice question on the lifecycle projection
All knobits of Data architecture.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 4: Data Architecture, element AV-12