General cognitive diagnostic models (CDM) such as the generalized deterministic input, noisy, "and" gate (G-DINA) model are flexible in that they allow for both compensatory and noncompensatory relationships among the subskills within the same test. Most of the previous CDM applications in the literature have been add-ons to simulation studies. Although there are some applications of CDMs such as the Fusion Model and the Rule Space Model to educational assessment data in general and second-language data in particular, there are few studies applying general models such as the G-DINA. The purpose of the present study was to demonstrate the application of the G-DINA to the reading comprehension data of a high-stakes test. To this end, an initial Q-matrix was developed, validated, and cross-validated. The skill profiles of the test takers were estimated using the "CDM" package in R. Throughout, the process of constructing and validating a Q-matrix was elaborated on, the benefits of general models were emphasized, and implications for research investigating inter-skill relationships were discussed. Finally, suggestions for further research, to better take advantage of the flexibilities of general diagnostic models, were presented.