Explains integration patterns and change data capture
Point-to-point, hub-and-spoke, publish-subscribe, ETL/ELT, batch, streaming, API; query-based vs log-based CDC and why the former misses hard deletes (DMBOK2 ch. 8).
Can AI do this?
AI does this well Most of the tested AI models master this. 3 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 | 98% | Not shown |
| 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: Point-to-point, hub-and-spoke, publish-subscribe, ETL/ELT, batch, streaming, API; query-based vs log-based CDC and why the former misses hard deletes (DMBOK2 ch. 8).
How it is assessed: Answers a written question on the integration patterns correctly
Related knobits
Needed for
- N-305 Recognises deletes invisible to change data capture
- N-308 Recognises a pattern too slow for its consumers
- N-333 Changes an integration pattern only where a requirement needs it
All 15 knobits of Design the integration patterns.
Source: Practice the stage 3 reference and review checklist rest on