Microsoft Azure AI Fundamentals (AI-901日本語版) : AI-901日本語

AI-901日本語 real exams

Exam Code: AI-901-JPN

Exam Name: Microsoft Azure AI Fundamentals (AI-901日本語版)

Updated: Nov 10, 2026

Q & A: 127 Questions and Answers

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Microsoft AI-901日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-900
Certificate Validity Period:1 year (renewable via free online assessment on Microsoft Learn)
Real Exam Qty:40-60
Exam Format:Multiple select, Scenario-based, Drag and drop, Multiple choice, Match the service to use case
Exam Price:USD 99
Related Certifications:Microsoft Certified: Azure AI Fundamentals
Exam Duration:45-60
Available Languages:Italian, Russian, English, French, Chinese (Simplified), Spanish, Indonesian (Indonesia), Korean, Chinese (Traditional), Japanese, Arabic (Saudi Arabia), Portuguese (Brazil), German
Passing Score:700 / 1000
Sample Questions:Free Download AI-901日本語 bootcamp pdf
Exam Way:Online (remote proctored via Pearson VUE) or at a Pearson VUE test center
Pre Condition:No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900

Microsoft AI-901日本語 Exam Syllabus Topics:

SectionWeightObjectives
Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify guiding principles for responsible AI
  • 1. Describe considerations for inclusiveness in an AI solution
  • 2. Describe considerations for reliability and safety in an AI solution
  • 3. Describe considerations for fairness in an AI solution
  • 4. Describe considerations for privacy and security in an AI solution
  • 5. Describe considerations for transparency in an AI solution
  • 6. Describe considerations for accountability in an AI solution
- Identify features of common AI workloads
  • 1. Identify features of document intelligence workloads
  • 2. Identify features of generative AI workloads
  • 3. Identify features of computer vision workloads
  • 4. Identify features of knowledge mining workloads
  • 5. Identify features of content moderation and moderation workloads
  • 6. Identify features of Natural Language Processing (NLP) workloads
Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Describe Azure Machine Learning capabilities
  • 1. Describe model management and deployment capabilities in Azure Machine Learning
  • 2. Describe data and compute services for data science and machine learning
  • 3. Describe capabilities of automated machine learning
- Describe core machine learning concepts
  • 1. Describe how training and validation datasets are used in machine learning
  • 2. Identify features and labels for training data in machine learning
- Identify common machine learning techniques
  • 1. Identify classification machine learning scenarios
  • 2. Identify regression machine learning scenarios
  • 3. Identify clustering machine learning scenarios
  • 4. Identify features of deep learning techniques
Describe features of computer vision workloads on Azure (15-20%)15-20%- Identify Azure tools and services for computer vision tasks
  • 1. Describe capabilities of the Azure AI Vision service
  • 2. Describe capabilities of the Azure AI Face detection service
- Identify common types of computer vision solution
  • 1. Identify features of object detection solutions
  • 2. Identify features of optical character recognition (OCR) solutions
  • 3. Identify features of image analysis solutions
  • 4. Identify features of semantic segmentation solutions
  • 5. Identify features of face detection and identification solutions
  • 6. Identify features of image classification solutions
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)15-20%- Identify features of common NLP workload scenarios
  • 1. Identify features and uses for key phrase extraction
  • 2. Identify features and uses for language modeling
  • 3. Identify features and uses for translation
  • 4. Identify features and uses for sentiment analysis
  • 5. Identify features and uses for speech recognition and synthesis
  • 6. Identify features and uses for entity recognition
- Identify Azure tools and services for NLP workloads
  • 1. Describe capabilities of the Azure AI Language service
  • 2. Describe capabilities of the Azure AI Speech service
Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify generative AI services and capabilities in Microsoft Azure
  • 1. Describe features and capabilities of Azure OpenAI service
  • 2. Describe features and capabilities of Azure AI Foundry model catalog
  • 3. Describe features and capabilities of Azure AI Foundry
- Identify features of generative AI solutions
  • 1. Identify common scenarios for generative AI
  • 2. Identify features of generative AI models
  • 3. Identify responsible AI considerations for generative AI

Microsoft AI-901日本語 Exam Questions Answered

The AI-901日本語 exam leads to the Microsoft Certified: Azure AI Fundamentals certification from Microsoft. It is positioned at the Fundamental level. The exam validates the knowledge and skills expected of candidates preparing for Microsoft Azure AI Fundamentals (AI-901日本語版).

Related certifications include:

  • Microsoft Certified: Azure AI Fundamentals

The AI-901日本語 exam includes 40-60 questions and allows 45-60. Before test day, divide the available time by the number of questions in a timed practice set to establish a steady per-question pace. If a difficult item starts consuming too much time, mark it for review and move forward instead of losing momentum. A full timed practice session can help you decide when to move on, when to return, and how much buffer to reserve for a final check.

The passing score for AI-901日本語 is 700 / 1000, and the official exam fee is USD 99. Because a retake requires paying the exam fee again, use timed practice results as a readiness check before scheduling your next attempt. If your performance remains inconsistent, continue reviewing weak domains and repeat a timed session before booking the exam.

No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial.

Eligibility rules can change, so confirm the current requirements on the official exam page before registering.

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The current AI-901日本語 outline is organized into 5 top-level domains. The first listed areas include:

  • Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) (15-20%)
  • Describe features of generative AI workloads on Azure (20-25%) (20-25%)
  • Describe Artificial Intelligence workloads and considerations (15-20%) (15-20%)

Review the Exam Topics section above for the complete outline and any nested subtopics.

Microsoft Azure AI Fundamentals (AI-901日本語版) Sample Questions:

Question #1

文を正しく完成させる選択肢を選びなさい。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.
Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.
The other options are incorrect:
Key phrase extraction identifies important phrases or main topics.
Language detection identifies the language of the text.
Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.

Question #2

あなたはAIベースの融資承認システムを開発しました。テスト中に、そのシステムに性別による偏りがあることが判明しました。
これは、責任あるAIのどの原則に違反しているのでしょうか?

  • A. 透明性
  • B. 信頼性と安全性
  • C. 説明責任
  • D. 公平性
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for PassTorrent members. You can sign-up / login (it's free).

Question #3

ユーザーからの問い合わせに対し、詳細な説明文で回答するAIソリューションを構築する必要があります。
どのタイプのAIモデルを使用すべきでしょうか?

  • A. 画像生成モデル
  • B. テキスト生成モデル
  • C. 埋め込みモデル
  • D. 音声認識モデル
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

文を正しく完成させる選択肢を選んでください。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library .
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.

Question #5

Microsoft Foundry ポータルで、Agent1 という名前のエージェントを作成し、そのエージェントをデプロイします。
Agent1をテストするために、Foundryのプレイグラウンドを開きます。
遊び場では、テストが実際の行動を反映していることを保証するために、どのような方法が用いられていますか?

  • A. エージェント1の設定
  • B. デプロイされなかった最後の保存済み構成ドラフト
  • C. エージェントの指示やツールを一切使用しないモデルの基本動作
  • D. プレイグラウンドによって生成されたデフォルト設定
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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