Study Databricks DA-ASSOC Model Fit: key concepts, common traps, and exam decision cues.
Databricks is testing whether you can recognize broad modeling fit for analytics. The right answer usually depends on how directly the model supports reporting and governed consumption, not on abstract model purity.
| Model | Best simple description |
|---|---|
| star schema | denormalized analytical model centered on facts and dimensions |
| snowflake schema | more normalized dimension structure around analytical facts |
| data vault | pattern focused on scalable, traceable enterprise data integration |
| medallion architecture | staged data quality and refinement flow, often described as bronze, silver, and gold |
| If the stem says… | Strong reading |
|---|---|
| “analytical reporting” | star or snowflake thinking often fits |
| “traceable integrated enterprise model” | data vault may fit |
| “align the model with Databricks medallion flow” | think staged refinement, not just table naming |
| Trap | Better rule |
|---|---|
| treating medallion as if it is a schema type by itself | medallion is a refinement architecture, not a direct substitute for every model |
| choosing the most complex model without a reason | pick the model that fits the workload and governance need |
Modeling questions usually reward choosing the model that fits analytics rather than just landing data. If the stem is about facts, dimensions, and business reporting, think star schema. If it is about staged refinement and trust, think medallion architecture as the pipeline pattern. The weak answer usually confuses the refinement stages with the downstream analytical model itself.