Investigating the processes underlying test performance is a major source of data supporting the explanation inference in the validity argument (Chappelle, 2021). One way of modeling the cognitive processes underlying test performance is by constructing a Q-matrix, which is essentially about summarizing the attributes explaining test-takers' response behavior. The current study documents the construction and validation of a Q-matrix for a high stakes test of reading within a generalized-deterministic inputs, noisy "and" gate (G-DINA) model framework. To this end, the attributes underlying the 20 items of the reading comprehension test were specified through retrospective verbal reports and domain experts' judgments. In the ensuing stage, the Q-matrix thus developed along with item response data of 2625 test-takers were subjected to empirical analysis using the procedure suggested by de la Torre and Chiu (2016). Item-level results showed that, except for one item, the processes underlying the other items were captured by compensatory and additive models. This finding has significant implications for model selection for DCM practitioners.