- Exam Code: AI-900
- Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
- Certification Provider: Microsoft
- Corresponding Certification:Microsoft Certified: Azure AI Fundamentals
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Certification Path
The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions:
您正在處理比賽中跑者的照片。
你需要讀取跑者T恤上的數字來辨識照片中的跑者。你應該使用哪種類型的電腦視覺？
- A. 臉部辨識
- B. 影像分類
- C. 光學字元辨識（OCR）
- D. 物體偵測
Correct Answer: C 🗳️
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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。
Correct Answer:
Explanation:
In Microsoft Azure AI Language Service, both Named Entity Recognition (NER) and Key Phrase Extraction are core features for text analytics. They serve distinct purposes in analyzing and structuring unstructured text data.
* Named Entity Recognition (NER):NER is used to identify and categorize specific entities within text, such as people, organizations, locations, dates, times, and quantities. According to Microsoft Learn's
"Analyze text with Azure AI Language" module, NER scans text to extract these entities along with their types. Therefore, the statement "Named entity recognition can be used to retrieve dates and times in a text string" is True (Yes).
* Key Phrase Extraction:This feature identifies the most important phrases or main topics in a block of text. It is useful for summarization or highlighting central ideas without classifying them into specific categories. Therefore, the statement "Key phrase extraction can be used to retrieve important phrases in a text string" is also True (Yes).
* City Name Retrieval:While key phrase extraction highlights major phrases, it does not extract specific entities like cities or dates. Extracting such details requires Named Entity Recognition, which is designed to find named entities such as city names, people, or organizations. Hence, the statement "Key phrase extraction can be used to retrieve all the city names in a text string" is False (No).
您正在為電子商務業務建立語言理解模型。
您需要確保模型能夠偵測到話語何時超出模型的預期範圍。
你該怎麼辦？
- A. 建立新模型
- B. 建立預建任務實體
- C. 將話語加入 None 意圖
- D. 使用新的話語測驗模型
Correct Answer: C 🗳️
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將 AI 工作負載類型與適當的場景相匹配。
若要回答，請將適當的工作負載類型從左側列拖曳到右側的場景。每種工作負載類型可以使用一次、多次或完全不使用。
注意：每個正確的選擇都值得一分。
Correct Answer:
Explanation:
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
您需要建立一個能夠識別圖像中名人的應用程式。
您應該使用哪種服務？
- A. 蔚藍阿爾視覺
- B. 會話語言理解（CLU）
- C. Azure 機器學習
- D. Azure OpenAI 服務
Correct Answer: A 🗳️
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