Recognises a check expression unable to run
An expression in logical names or business terms rather than physical column names, in another dialect's syntax, or reading columns the rule does not cite. It either errors — and an erroring check is often counted as passing — or runs against something else.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 64% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 100% | Mastered |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 99% | Mastered |
| gpt-6.1-sol* | Mid tier | 99% | Mastered |
| claude-sonnet-5-5* | Mid tier | 99% | Mastered |
| gemini-3.5-flash-lite | Low cost | 64% | Not shown |
| gpt-6-luna | Low cost | 83% | Not shown |
| claude-haiku-5-5 | Low cost | 83% | Not shown |
* From the earlier benchmark of 2026-10: 10 data-modelling test tasks, 3 runs per task, graded by claude-opus-5-5. These models have not taken the current 123 tasks yet; the two methods differ, so compare their figures with care. They are left out of the “most models” counts and the best-model picks.
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 expression and its runnable physical form
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "Makse summa" <= "Kontojääk" against a table whose columns are makse_summa and kontojaak; or Postgres ~ regex on a Snowflake target Acceptable: makse_summa <= kontojaak, with both columns in columns; dialect-specific syntax noted in comment (DQR-010, DQR-020)
Also known as: Expression that cannot run (data quality rule)
Related knobits
Builds on
All 30 knobits of Derive data quality rules.
Source: Review checklist 03-dq-rules (data-quality), row 17; DMBOK2; this skill; formerly data-modelling K-717