Recognises an RTO longer than a service's impact tolerance
A data set's RTO exceeds the impact tolerance of a critical service that needs it.
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 | 72% | Not shown |
| gpt-6-luna | Low cost | 72% | 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: The defect named on the RTO beyond the tolerance of a dependent service, and its fix or the question for its owner
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: DS-003 card database RTO 45 min; card authorisation tolerance 30 min Acceptable: RTO within 30 min with headroom for the application, or the gap raised with the Head of Cards
Also known as: RTO longer than a service's tolerance
Related knobits
Builds on
All 14 knobits of Plan backup, recovery and continuity.
Source: Review checklist 03-backup-recovery-continuity, row 2 (DORA Art. 11; 22301)