Learning deterministic finite automata (DFAs) from labeled traces is a key problem with applications in software analysis and system modeling. SAT-based methods are effective but can be slow when dealing with large datasets. To address this, we propose a sampling method that selects a smaller, but still representative set of traces. Our approach groups traces with similar suffixes and uses edit distance to choose diverse examples. The proposed sampling performs better than random uniform sampling and significantly better than heuristic algorithms.