[{"data":1,"prerenderedAt":47},["ShallowReactive",2],{"q-fintech-22-057":3},{"exam":4,"examName":5,"chapterSlug":6,"question":7,"related":22},"fintech","金融科技力知識檢定","c4",{"id":8,"exam":4,"session":9,"qno":10,"question":11,"options":12,"answer":17,"chapter":18,"freq":19,"explanation":20,"difficulty":21},"fintech-22-057","22",57,"金融機構常使用的「信用評分模型」中，應用 AI 的主要優勢是什麼？",[13,14,15,16],"增加運算時間","提高模型透明度","自動學習非線性關係並改善預測準確率","降低客戶隱私保護水準",2,"人工智慧",1,"傳統信用評分多採邏輯斯迴歸等線性模型,變數關係需人工設定。AI(如梯度提升樹、神經網路)能自動從大量資料中學習變數間複雜的非線性關係與交互作用,並可納入交易行為等替代數據,提升違約預測準確率,故 (C) 正確。(A) 增加運算時間不是優勢;(B) 錯,複雜的 AI 模型多屬黑箱,透明度與可解釋性反而較低,需另以可解釋 AI 補強;(D) 降低客戶隱私保護水準是風險而非優勢,運用 AI 仍須遵守個資保護規範。","easy",[23,27,31,35,39,43],{"id":24,"question":25,"qno":26},"fintech-19-006","1980 年代人工智慧發展出的專家系統(Expert System)把取自專家的資訊彙編成一組規則，這些規則往往以「若...則...」結構呈現，稱為下列何種規則？",6,{"id":28,"question":29,"qno":30},"fintech-19-007","下列何種技術可以讓電腦模仿人類大腦的運算，讓電腦像人一樣思考？",7,{"id":32,"question":33,"qno":34},"fintech-19-008","有關機器學習訓練與預測的敘述，下列何者錯誤？",8,{"id":36,"question":37,"qno":38},"fintech-19-036","下列何種機器學習是所有資料都沒有標註，機器透過尋找資料的特徵，自己進行分類？",36,{"id":40,"question":41,"qno":42},"fintech-19-040","人工智慧使用運算晶片如 GPU，未來運用在特定人工智慧特定應用領域的專用晶片，稱為下列何者？",40,{"id":44,"question":45,"qno":46},"fintech-19-044","有關 2023 年推出新一代的語言模型 GPT-4 屬性之敘述，下列何者正確？",44,1793070566507]