Microsoft AI-900日本語 exam : Microsoft Azure AI Fundamentals (AI-900日本語版)

AI-900日本語 Exam Simulator
  • Exam Code: AI-900J
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
  • Updated: Nov 09, 2026
  • Q & A: 336 Questions and Answers

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Budget for the Microsoft Azure AI Fundamentals (AI-900日本語版) exam once, not twice. Retakes bill at the full registration fee, while Exam4PDF's AI-900日本語 practice questions cost a fraction of that and target exactly the gaps that cause retakes.

Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
  • 1. Key phrase extraction
    • 2. Language modeling and translation
      • 3. Sentiment analysis
        Topic 2: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
        • 1. Training and validation concepts
          • 2. Supervised vs unsupervised learning
            Topic 3: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
            • 1. Common AI workloads
              • 2. Responsible AI principles
                Topic 4: Features of computer vision workloads on Azure15-20%- Computer vision solutions
                • 1. Image classification
                  • 2. Object detection
                    • 3. OCR and image analysis

                      The Microsoft AI-900日本語 Exam, Question by Question

                      The AI-900日本語 exam is how Microsoft decides who earns the Microsoft Certified: Azure AI Fundamentals certification, which sits at the Fundamentals level. It measures practical command of the Microsoft Azure AI Fundamentals (AI-900日本語版) objectives — the skills you'd actually use on the job — which is why it carries weight with employers in a competitive market. It's also part of a wider certification family that includes Microsoft Azure Data Fundamentals, Microsoft Azure Fundamentals, Microsoft Azure AI Engineer Associate, so the effort you invest here keeps paying off as you advance. Candidates do best when they treat the exam as a skills check, not a memory test.

                      Microsoft organizes the Microsoft Azure AI Fundamentals (AI-900日本語版) syllabus into 4 domains, opening with Fundamentals of machine learning on Azure (25-30%), Features of natural language processing (NLP) workloads on Azure (30-35%), and Features of computer vision workloads on Azure (15-20%). Those weightings are a gift: they tell you where the points concentrate, so you can front-load the domains that decide your score. The full breakdown lives in the exam topics section above — build your study plan around it.

                      Expect 40-60 questions within 85 minutes on the Microsoft Azure AI Fundamentals (AI-900日本語版) exam. Treat that ratio as a pacing contract: work out your per-question budget now, and during the exam, flag anything that threatens to overrun it and circle back later. Candidates who rehearse this under a real timer — the Exam4PDF test engine runs one on every mock exam — consistently finish with time to review instead of guessing through the final questions.

                      You'll need 700/1000 to pass the Microsoft Azure AI Fundamentals (AI-900日本語版) exam, and a seat costs USD 99 (varies by region). Since retakes bill the full fee again, the economics are simple: arrive over-prepared. The reliable method is timed self-testing — run Exam4PDF practice exams until your scores sit clearly above the passing mark across several sessions, then register. You're not buying certainty; you're refusing to pay for the same exam twice.

                      Microsoft lists the Microsoft Azure AI Fundamentals (AI-900日本語版) question types as: Multiple choice, Multiple response, Drag and drop, Case studies. Knowing the list is half the battle; the other half is practicing against each type until its mechanics are boring. For lengthy scenario items, anchor on the actual question sentence before digesting the stem. Exam4PDF's 336 practice questions cover these formats, so nothing on your screen in the exam room is a first encounter.

                      The Microsoft Azure AI Fundamentals (AI-900日本語版) exam is currently available in English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil). Pick the language where your reading speed is highest — in a timed exam, comprehension pace quietly decides borderline scores. If you're testing in English, Exam4PDF's English-language AI-900日本語 practice questions double as drills for the exact terminology Microsoft favors.

                      Passing the Microsoft Azure AI Fundamentals (AI-900日本語版) exam earns you a credential that stays valid for Does not expire (Fundamentals certification). Put the expiry date somewhere you'll see it — renewal is painless when planned and painful when discovered late. Because Microsoft owns the recertification policy and revises it from time to time, check the official certification page well before your renewal window opens.

                      All three versions carry the same 336 expert-researched questions — pick the experience, not the content:

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                      Yes — and we encourage it. Exam4PDF's free Microsoft Azure AI Fundamentals (AI-900日本語版) PDF demo shows you genuine sample questions and full verified answers before any payment. Every purchase then includes 365 days of free updates, with renewal afterward at 50% off from your member zone. Judge the material first; that's what the demo is for.

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                      What You Get with Exam4PDF for Microsoft Azure AI Fundamentals (AI-900日本語版)

                      A preparation system, not just a file. Judge the quality first with the free demo; once you buy, your AI-900日本語 material is in your inbox within one minute of payment (contact support if 2 hours pass), installs on unlimited computers, and stays current through 365 days of free updates. Transactions are protected by McAfee security services, and your personal information is never shared with third parties.

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                      Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:

                      Question #1

                      Form Recognizer でカスタム モデルを使用する利点は何ですか?

                      • A. カスタム モデルは常に高い精度を提供します。
                      • B. カスタム モデルは、さまざまなフォーム タイプを認識するようにトレーニングできます。
                      • C. オンプレミスにデプロイできるのはカスタム モデルのみです。
                      • D. カスタム モデルは、事前構築されたモデルよりも安価です。
                      Reveal Solution  Discussion  0

                      Correct Answer: B  🗳️

                      Explanation: Only visible for Exam4PDF members. You can sign-up / login (it's free).

                      Question #2

                      あなたの会社は、スマートホームデバイスでの音声認識テクノロジーの使用を検討しています。同社は、特定のユーザーグループを意図せずに除外する可能性のある障壁を特定したいと考えています。
                      これは、責任あるAIのためのマイクロソフトの指導原則の例ですか?

                      • A. 包括性
                      • B. プライバシーとセキュリティ
                      • C. 公平性
                      • D. 説明責任
                      Reveal Solution  Discussion  0

                      Correct Answer: A  🗳️

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                      Question #3

                      コードを分析し、コード関数とコードコメントの説明を生成するために使用できる 2 つのリソースはどれですか? それぞれの正解は完全なソリューションを示します。
                      注意: 正解ごとに 1 ポイントが付与されます。

                      • A. Azure OpenAI GPT-4 モデル
                      • B. Azure OpenAI DALL-E モデル
                      • C. GitHub Copilot サービス
                      • D. Azure OpenAI Whisper モデル
                      Reveal Solution  Discussion  0

                      Correct Answer: A,C  🗳️

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                      Question #4

                      文を完成させるには、回答領域で適切なオプションを選択します。

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe core concepts of machine learning on Azure", labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.
                      In this scenario, the task is to assign classes to images before training a classification model-for example, marking images as "cat," "dog," or "bird." This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.
                      Microsoft's official materials clearly define labeling as:
                      "The process of tagging data with the correct answer so that the model can learn to make predictions." Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.
                      Let's briefly clarify why the other options are incorrect:
                      * Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.
                      * Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.
                      * Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.
                      Thus, assigning classes to images prior to model training is definitively a Labeling task.

                      Question #5

                      次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
                      注:正しい選択はそれぞれ1ポイントの価値があります。

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:
                      # Yes - Extract key phrases
                      # No - Generate press releases
                      # Yes - Detect sentiment
                      The Azure AI Language service is a powerful set of natural language processing (NLP) tools within Azure Cognitive Services, designed to analyze, understand, and interpret human language in text form. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, this service includes several capabilities such as key phrase extraction, sentiment analysis, language detection, named entity recognition (NER), and question answering.
                      * Extract key phrases from documents # YesThe Key Phrase Extraction feature identifies the most relevant words or short phrases within a document, helping summarize important topics. This is useful for indexing, summarizing, or organizing content. For instance, from "Azure AI Language helps analyze customer feedback," it may extract "Azure AI Language" and "customer feedback" as key phrases.
                      * Generate press releases based on user prompts # NoThis functionality falls under generative AI, specifically within Azure OpenAI Service, which uses models such as GPT-4 for text creation. The Azure AI Language service focuses on analyzing and understanding existing text, not generating new content like press releases or articles.
                      * Build a social media feed analyzer to detect sentiment # YesThe Sentiment Analysis capability determines the emotional tone (positive, neutral, negative, or mixed) of text data, making it ideal for analyzing social media posts, reviews, or feedback. Businesses often use this to gauge customer satisfaction or brand reputation.
                      In summary, the Azure AI Language service analyzes text to extract insights and detect sentiment but does not generate new textual content.

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