由頂級 ISTQB 專家團隊以最高技術水平整理製作，確保 CT-AI 題庫的專業性和準確性。

本題庫源自我們的 ISTQB 專家對目前最新的 CT-AI 輔導指南的整編，可以對您的學習起到極大的幫助作用。我們的團隊成員都是來自指定認證專家、培訓師和 ISTQB 相關工作從業者，他們對 CT-AI 考試內容和 ISTQB 認證要求的資歷瞭如指掌，這樣可以確保 CT-AI 練習題庫的高質量。你只需在參加 CT-AI 考試前认真學習下本題庫，將有助於順利完成考試。

ISTQB CT-AI 考試概覽:

認證廠商:	ISTQB
考試名稱:	ISTQB 認證測試師 — 人工智慧測試考試
考試代碼:	CT-AI
考試時間:	60分鐘（非母語考生為75分鐘）
證照有效期限:	永久有效（無效期限制）
實際考試題數:	40題
考試形式:	選擇題, 每題1至2分
支援語言:	英文, 德文, 法文, 西班牙文, 葡萄牙文, 中文, 日文, 韓文
相關認證:	ISTQB 認證測試師基礎級（CTFL） ISTQB 認證測試師生成式人工智慧測試（CT-GenAI）
及格分數:	65%（2.0版需29/44分；1.0版需31/47分）
考試費用:	180歐元至250歐元（依地區與報考機構而定）
推薦課程:	ISTQB 認可訓練機構 CT-AI 考試大綱 2.0版
考試報名:	iSQI 考試報名 Pearson VUE ISTQB 官方報名
範例考題:	ISTQB CT-AI 範例考題
考試方式:	線上遠端監考 / 實體考場應試
必備條件:	需持有 ISTQB 認證測試師基礎級（CTFL）證書
官方大綱網址:	https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/

ISTQB CT-AI 考試大綱：

主題	簡介
主題 1	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
主題 2	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
主題 3	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
主題 4	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
主題 5	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
主題 6	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.