Explains archiving as an integration flow
Moving rarely used data to cheaper stores and keeping it accessible.
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 | 75% | Not shown |
| gemini-3.5-flash-lite | Low cost | 44% | Not shown |
| gpt-6-luna | Low cost | 75% | Not shown |
| claude-haiku-5-5 | Low cost | 75% | 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: Moving rarely used data to cheaper stores and keeping it accessible.
How it is assessed: Answers a CDMP-style multiple-choice question on the archiving flows
All knobits of Data integration and interoperability.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 8: Data Integration and Interoperability, element NV-9