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Who should take the AI-900: Microsoft Azure AI Fundamentals Exam
High level of difficulty and many challenging questions. Identify the different types of AI models. AI design choosing tools. Who should take the exam? Artificial intelligence. Describe the strengths and weaknesses for artificial intelligence.
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Exam Overview
This certification test is available in English, Spanish, Simplified Chinese, Japanese, French, Korean, and German. You will find different formats of questions while dealing with this Microsoft exam. These include multiple choice, drag and drop, build list, active screen, short answer, and best answer. The test costs $99 and the learners can register for it through Pearson VUE or Certiport.
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Prerequisites
Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.
The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify Azure AI services for NLP - Identify common NLP tasks |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 5: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks |
Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
The correct answer is Document Intelligence.
According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft Learn documentation, the Azure AI Document Intelligence service (formerly known as Form Recognizer) is specifically designed to extract structured data from documents, including scanned invoices, receipts, forms, and business cards.
This service combines optical character recognition (OCR) with machine learning to analyze both the layout and semantic meaning of document content. When processing scanned invoices, Document Intelligence identifies and extracts fields such as invoice numbers, dates, totals, taxes, vendor names, and line-item details.
The extracted information can then be automatically imported into business systems like accounting software or databases, eliminating manual data entry and improving operational efficiency.
Here's why the other options are incorrect:
* Generative AI: Focuses on creating new content such as text, images, or code (for example, using GPT-
4 or DALL E). It is not used for structured data extraction.
* Natural Language Processing (NLP): Deals with understanding and generating human language from text-based input, not document scanning or layout interpretation.
The Document Intelligence workload excels at handling semi-structured documents where the location and format of data vary between samples. Microsoft's prebuilt models-like Invoice, Receipt, Identity Document, and Contract-simplify extraction without requiring custom training.
In summary, if the task involves extracting data from scanned invoices, the appropriate Azure AI service is Azure AI Document Intelligence, which uses AI-powered document understanding to convert unstructured document images into structured, usable data.
Microsoft の責任ある AI 原則の例は何ですか?
- A. AI システムではブラックボックス モデルを使用する必要があります。
- B. AIシステムは人々を公平に扱うべきです。
- C. AI システムは設計の詳細を公開すべきではありません。
- D. AI システムは開発者の利益を保護する必要があります。
Correct Answer: B 🗳️
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Microsoft Foundryを使用して、自動車のナンバープレートを読み取るAIアプリケーションを開発する予定です。このアプリケーションの開発には、何を使用すべきでしょうか？
- A. Microsoft Visual Studio Code
- B. Microsoft 365 用 Copilot
- C. Azure Al Studio
- D. GitHub Actions
Correct Answer: C 🗳️
機械学習における回帰モデルの例は何ですか?
- A. 画像解析による絶滅危惧動物の個体数の特定
- B. 過去のデータ、家の大きさ、寝室の数に基づいて家の販売価格を予測する
- C. ユーザーによってフラグが付けられた大量のメールを調べて、スパムメールのサブタイプを識別する
- D. データセット内の生徒データを生徒の年齢と学業成績に基づいて分割する
Correct Answer: B 🗳️
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レース中のランナーの写真を処理しています。
写真に写っているランナーを識別するには、ランナーのシャツに書かれた番号を読み取る必要があります。どのタイプのコンピュータービジョンを使用すればよいでしょうか？
- A. 物体検出
- B. 画像分類
- C. 光学文字認識（OCR）
- D. 顔認識
Correct Answer: C 🗳️
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