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This article illustrates some effects of dynamic adaptive design in a large government survey. We present findings from the 2015 National Survey of College Graduates Adaptive Design Experiment, including results and discussion of sample representativeness, response rates, and cost. We also consider the effect of truncating data collection (examining alternative stopping rules) on these metrics. In this experiment, we monitored sample representativeness continuously and altered data collection procedures—increasing or decreasing contact effort—to improve it. Cases that were overrepresented in the achieved sample were assigned to more passive modes of data collection (web or paper) or withheld from the group of cases that received survey reminders, whereas underrepresented cases were assigned to telephone follow-ups. The findings suggest that a dynamic adaptive survey design can improve a data quality indicator (R-indicators) without increasing cost or reducing response rate. We also find that a dynamic adaptive survey design has the potential to reduce the length of the data collection period, control cost, and increase timeliness of data delivery, if sample representativeness is prioritized over increasing the survey response rate.