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Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions (Q120-Q125):
NEW QUESTION # 120
對於以下每個陳述,如果該陳述為真,請選擇「是」。否則,選擇“否”。 注意:每個正確的選擇都值得一分。
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
NEW QUESTION # 121
您應該使用哪種服務從掃描文檔中自動提取文本、鍵/值對和表格數據?
- A. 文本分析
- B. 表單識別器
- C. 墨跡識別器
- D. 自定義視覺
Answer: B
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
NEW QUESTION # 122
您需要為業務聊天機器人提供有助於回答簡單用戶查詢的內容。
使用 QnA Maker 創建問題和答案文本的三種方法是什麼?每個正確答案都代表一個完整的解決方案。
注意:每個正確的選擇都值得一分。
- A. 手動輸入問題和答案。
- B. 將機器人連接到 Cortana 通道並使用 Cortana 提問。
- C. 從預定義的數據源導入聊天內容。
- D. 從現有網頁生成問題和答案。
- E. 使用自動化機器學習根據包含問題的文件來訓練模型。
Answer: A,C,D
Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION # 123
您需要為業務聊天機器人提供有助於回答簡單用戶查詢的內容。
使用 QnA Maker 創建問題和答案文本的三種方法是什麼?每個正確答案都代表一個完整的解決方案。
注意:每個正確的選擇都值得一分。
- A. 手動輸入問題和答案。
- B. 將機器人連接到 Cortana 通道並使用 Cortana 提問。
- C. 從預定義的數據源導入聊天內容。
- D. 從現有網頁生成問題和答案。
- E. 使用自動化機器學習根據包含問題的文件來訓練模型。
Answer: A,C,D
Explanation:
自動提取
從半結構化內容中提取問答對,包括常見問題解答頁面、支持網站、Excel 文件、SharePoint 文檔、產品手冊和政策。
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION # 124
您將圖像發送到電腦視覺 API 並接收回展覽中顯示的註釋的圖像。
使用了哪種類型的電腦視覺?
- A. 影像分類
- B. 光學字元辨識 (OCR)
- C. 語意分割
- D. 物體偵測
Answer: D
Explanation:
物件偵測與標記類似,但 API 傳回每個找到的物件的邊界框座標(以像素為單位)。例如,如果影像包含狗、貓和人,則偵測操作將列出這些物件及其在影像中的座標。您可以使用此功能來處理影像中物件之間的關係。它還可以讓您確定圖像中是否存在同一標籤的多個實例。
Detect API 根據影像中識別的物件或生物應用標籤。目前標記分類法和物件檢測分類法之間沒有正式的關係。在概念層面上,Detect API 僅尋找物件和生物,而 Tag API 還可以包含上下文術語,例如“室內”,這些術語無法使用邊界框進行本地化。
參考:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 125
......
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