The demand for diagnostic feedback has triggered extensive research on cognitive diagnostic models (CDMs), such as the deterministic input, noisy output "and" gate (DINA) model. This study explored two Q-matrix specifications with the DINA model in a statewide large-scale mathematics assessment. The first Q-matrix was developed based on five predefined content reporting categories, and the second was based on the post hoc coding of 15 attributes by test-development experts. Total raw scores correlated strongly with the number of skills mastered, using both Q-matrices. Correlations between the DINA-model item statistics and those from the item response theory analyses were moderate to strong, but were always lower for the 15-skill model. Results highlighted the trade-off between finer-grained modeling and less precise model estimation.