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The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:
- Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
- Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.
- Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
- Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
- Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 2: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 3: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 4: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions:
您應該如何減少機器學習分類模型產生的誤報數量?
- A. 在訓練資料中包含測試資料。
- B. 修改閾值以支援誤報。
- C. 增加訓練迭代次數。
- D. 修改閾值以支援誤報。
Correct Answer: D 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
您需要識別資料集中具有相似數值的行組。您應該使用哪種類型的機器學習?
- A. 聚類
- B. 回歸
- C. 分類
Correct Answer: A 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
您正在評估是在 Azure 機器學習中使用基本工作區還是企業工作區。
哪兩項任務需要企業工作空間?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. 使用圖形使用者介面 (GUI) 執行自動化機器學習實驗。
- B. 從逗號分隔值 (CSV) 檔案建立資料集。
- C. 建立一個計算實例以用作工作站。
- D. 使用圖形使用者介面 (GUI) 從 Azure 機器學習設計器定義和執行機器學習實驗。
Correct Answer: A,D 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
可以使用哪兩種語言為 Azure 機器學習設計器編寫自訂程式碼?每個正確答案都代表一個完整的解決方案。
筆記; 每個正確的選擇都值得一分。
- A. 斯卡拉
- B. C#
- C. R
- D. Python
Correct Answer: C,D 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
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:
The Azure Text Analytics service, a component of Azure Cognitive Services, provides natural language processing (NLP) capabilities to analyze and understand text-based data. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features and uses for Natural Language Processing (NLP)", the Text Analytics service supports multiple text understanding tasks, such as language detection, key phrase extraction, sentiment analysis, and entity recognition.
* Language Identification - Yes:Text Analytics can automatically detect the language in which text is written. This feature analyzes linguistic patterns and assigns a language code (for example, "en" for English, "es" for Spanish). It is one of the primary features described in Microsoft Learn as part of the service's Language Detection API.
* Detect Handwritten Signatures - No:Detecting handwritten signatures is not a text-based NLP task.
Instead, it belongs to the computer vision domain, specifically Optical Character Recognition (OCR).
The Text Analytics service only processes digital text, not handwritten or image-based data. To detect handwriting or signatures, you would use the Computer Vision OCR API, not Text Analytics.
* Entity Recognition - Yes:The Text Analytics service can identify named entities-such as people, locations, organizations, dates, and quantities-within documents. This is known as Named Entity Recognition (NER), which helps extract structured information from unstructured text.
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