Query AutoComplete (QAC) is an important feature in e-commerce search engines, aimed at enhancing user experience by offering relevant query suggestions. However, these suggestions often include semantically duplicate entries derived from user logs. While the existing literature has made significant progress in query similarity learning for e-commerce applications, the specific challenge of query deduplication has received less attention. To address this issue, this paper presents a new industry-scale framework for QAC deduplication at Coupang, utilizing diverse data augmentation techniques to enhance deduplication accuracy effectively. Our results reveal that this approach substantially outperforms existing query similarity methods, providing valuable insights into the utility of various pre-trained models and data augmentation strategies. Online A/B testing further validates the significant impact of our deduplication framework on improving the e-commerce search experience, highlighting the importance of addressing semantic duplicates in QAC suggestions and offering a practical solution with proven effectiveness in a live e-commerce environment.