gemini-4-argon
gemini-4-argon masters 167 of the 199 benchmarked knobits of Data modelling: an expected 93% of the competence.
Data modelling, activity by activity
| Activity | Test score | Knobits mastered (expected) |
|---|---|---|
| Capture data requirements | 87% | 88% |
| Define business terms | 85% | 88% |
| Document data rules as SBVR fact types and rule statements | 86% | 97% |
| Build the conceptual model and taxonomies | 98% | 100% |
| Derive the logical data model | 85% | 95% |
| Design the physical data model | 100% | 98% |
| Derive data quality rules | 88% | 95% |
| Generate and review DDL | 77% | 82% |
Where it falls short
The knobits with the lowest probability of mastery: review its output here most carefully.
- K-133 Recognises a missing or wrong requirement trace 0%
- K-816 Recognises one decimal scale for every currency 0%
- K-209 Recognises a hidden definition of a second concept 0%
- K-211 Recognises a definition carrying non-defining content 10%
- K-810 Recognises a truncated identifier taken as the real name 19%
- K-718 Recognises a misfiled data quality dimension 20%
- K-821 Recognises a restricted column without access design 20%
- K-117 Recognises a missing or wrong requirement type 20%
- K-116 Recognises a missing or meaningless requirement priority 39%
- K-820 Recognises a default inventing a business fact 40%
- K-719 Recognises duplicate checks for one rule 41%
- K-808 Recognises a CHECK constraint parsed and ignored 41%
- K-503 Recognises a deletion anomaly in a logical data model 42%
- K-501 Recognises an insertion anomaly in a logical data model 43%
- K-531 Traces a logical-model anomaly to its structural cause 45%