Question 1 of 17
How would you choose between an embedding model with a larger context window and a smaller-context model when chunk sizes are also configurable?
Strong Interview Answer
Start by ensuring chunks do not exceed the candidate embedding input limit, then evaluate retrieval and end-to-end answer quality on representative queries. A longer context window is useful only if the application needs it; if smaller chunks with the 512-token model measurably retrieve exact error codes better, choose the measured winner rather than the largest window by default.
What to Listen For
- embedding input limit
- chunk-size/context coupling
- representative retrieval evaluation
- measured quality over theoretical capacity
Caution
Look for reasoning tied to the stated Databricks component and trade-off; a product name without the decision logic is incomplete.