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

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Exam Code: AI-900J

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

Updated: Jul 29, 2026

Q & A: 336 Questions and Answers

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The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.

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Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

  • Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
  • Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
  • Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.
  • Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

Microsoft AI-900日本語 Exam Syllabus Topics:

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