Microsoft : AI-901日本語 Questions & Answers

Last Updated: Oct 07, 2026

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Microsoft AI-901日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Available Languages:Japanese, French, English, Portuguese (Brazil), Chinese (Traditional), German, Spanish, Chinese (Simplified), Korean
Certificate Validity Period:Does not expire (Fundamentals certification)
Real Exam Qty:40-60
Exam Price:Approx. 99 USD (varies by region)
Passing Score:700 (out of 1000)
Related Certifications:Microsoft Azure Data Fundamentals (DP-900)
Microsoft Azure Fundamentals (AZ-900)
Exam Format:Drag and drop, Multiple choice, Case study (limited), Multiple response
Exam Duration:60 minutes
Recommended Training:Azure AI Fundamentals Course
Microsoft Learn - AI-900 Learning Path
Exam Registration:Schedule exam via Pearson VUE
Microsoft Certification Portal
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

Microsoft AI-901日本語 Exam Syllabus Topics:

SectionWeightObjectives
Describe Artificial Intelligence workloads and considerations20-25%- Identify features of common AI workloads
  • 1. Natural language processing use cases
    • 2. Conversational AI and chatbots
      • 3. Document processing and extraction
        • 4. Computer vision use cases
          - Responsible AI principles
          • 1. Privacy and security
            • 2. Reliability and safety
              • 3. Transparency and accountability
                • 4. Inclusiveness
                  • 5. Fairness
                    Describe features of computer vision workloads on Azure15-20%- Azure AI Vision services
                    • 1. Face detection and analysis (limited use cases)
                      • 2. Azure AI Vision
                        - Image analysis
                        • 1. Optical character recognition (OCR)
                          • 2. Image classification
                            • 3. Object detection
                              Describe features of generative AI workloads on Azure10-15%- Azure OpenAI and generative services
                              • 1. Azure OpenAI Service capabilities
                                • 2. Use cases: chatbots, content generation, summarization
                                  - Generative AI concepts
                                  • 1. Responsible use of generative AI
                                    • 2. Prompt engineering basics
                                      • 3. Large language models (LLMs)
                                        Describe features of natural language processing (NLP) workloads on Azure15-20%- Text analysis
                                        • 1. Key phrase extraction
                                          • 2. Language detection
                                            • 3. Sentiment analysis
                                              - Azure NLP services
                                              • 1. Azure AI Language
                                                • 2. Speech services (speech-to-text, text-to-speech)
                                                  Describe fundamental principles of machine learning on Azure25-30%- Azure Machine Learning basics
                                                  • 1. AutoML concepts
                                                    • 2. Azure ML workspace components
                                                      - Machine learning concepts
                                                      • 1. Regression and classification
                                                        • 2. Supervised vs unsupervised learning
                                                          • 3. Training and evaluation concepts

                                                            AI-901日本語 Exam Prep: Strategy, Details, and Support

                                                            The Microsoft Azure AI Fundamentals (AI-901日本語版) blueprint is structured around these main domains:

                                                            • Describe features of computer vision workloads on Azure (15-20%)
                                                            • Describe features of generative AI workloads on Azure (10-15%)
                                                            • Describe features of natural language processing (NLP) workloads on Azure (15-20%)

                                                            Additional domains complete the official outline — our question bank spans the full set.

                                                            According to the latest exam information, the AI-901日本語 exam contains 40-60 questions with a 60 minutes-minute limit. Since performance on the day matters as much as preparation, timed practice under these exact conditions is time well spent.

                                                            Microsoft points candidates toward these official training options:

                                                            Formal training builds the framework; consistent question practice fills it in.

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                                                            Currently, the AI-901日本語 exam's passing score is 700 (out of 1000) and the registration fee is Approx. 99 USD (varies by region). Microsoft owns both figures and may update them, so verify on the official site when you book.

                                                            By treating preparation and performance as one campaign. The AI-901日本語 bank is rebuilt around every exam change — new key points and the latest question types compiled by our first-class experts, with expert-verified answers across the Microsoft Azure AI Fundamentals (AI-901日本語版) objectives — so what you practice matches what you will face. Behind the content stands our service idea: customers' benefits first. Professionally trained after-sale staff answer 24/7, updates stay free for 365 days, and a written refund policy protects your investment throughout.

                                                            Microsoft specifies these prerequisites for the Microsoft Azure AI Fundamentals (AI-901日本語版): No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended..

                                                            Verify the current requirements on the official certification page.

                                                            Registration goes through the official channels listed here:

                                                            Book a test center or online slot that fits your schedule — and leave yourself room to prepare calmly.

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                                                            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:
                                                            The correct answer is endpoint of the model . After a vision-enabled GPT model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint that applications can call programmatically. The application sends requests containing text and, when required, image content to the deployed model through the appropriate chat-completions API endpoint. Microsoft's documentation explicitly states that to use vision- enabled models, you call the Chat Completion API against a supported model that has been deployed.
                                                            The Foundry playground is primarily an interactive environment for testing prompts, model behavior, and multimodal inputs during development. It is not the production interface that an external application uses to submit inference requests. An evaluation pipeline is used to assess model or application quality against defined metrics and datasets, while a training dataset is associated with model training or fine-tuning rather than serving inference traffic.
                                                            Microsoft Foundry provides the endpoint and required authentication information after deployment.
                                                            Applications then invoke that endpoint using an SDK or REST API, passing text and image inputs as part of the request payload.

                                                            Question #2

                                                            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:

                                                            The Model catalog is used for comparing and deploying a wide range of models for generative AI development.
                                                            Microsoft states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications. Microsoft training also states that the model catalog is used to compare models by benchmark metrics and deploy models to endpoints.

                                                            Question #3

                                                            あなたは自社におけるAI活用のベストプラクティスを検討しています。
                                                            各タスクは、マイクロソフトの責任あるAI原則のどれに該当するでしょうか?回答するには、適切な原則を正しいタスクにドラッグしてください。各タスクは、1回、複数回、またはまったく使用されない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
                                                            注:正解1つにつき1ポイントが加算されます。

                                                            Reveal Solution  Discussion  0

                                                            Correct Answer:


                                                            Explanation:
                                                            * Fairness
                                                            * Privacy and security
                                                            * Transparency
                                                            * Reliability and safety
                                                            Comprehensive and Detailed Explanation with all Azure AI documents : =
                                                            Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
                                                            Fairness
                                                            Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
                                                            Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
                                                            Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
                                                            Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.

                                                            Question #4

                                                            Foundry Tools の Azure Content Understanding を使用して請求書からフィールドを抽出する Python アプリケーションがあります。アナリスト向けに PDF を送信します。
                                                            アプリケーションは結果を取得するために何をするべきでしょうか?

                                                            • A. Microsoft FoundryエンドポイントにGETリクエストを送信して、アナライザースキーマをダウンロードします。
                                                            • B. 非同期解析ジョブが成功するまで、操作結果をポーリングします。
                                                            • C. 光学文字認識(OCR)を使用してPDFからテキストを解析し、コード内のフィールドをマッピングします。
                                                            • D. 同期応答を強制するために、同じ入力で begin_analyze() を繰り返し呼び出します。
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: B  🗳️

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

                                                            Question #5

                                                            Azure Vision in Foundry Toolsサービスを使用すると、どのような操作を実行できますか?

                                                            • A. 手書き文字からのデータ抽出
                                                            • B. トレーニングビデオのサムネイルを作成する
                                                            • C. 文書からキーワードを抽出する
                                                            • D. ライブビデオストリームで動物の品種を識別する
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: A  🗳️

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

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