Microsoft Designing and Implementing an Azure AI Solution (AI-100日本語版) : AI-100日本語 valid dumps

AI-100日本語 real exams

Exam Code: AI-100J

Exam Name: Designing and Implementing an Azure AI Solution (AI-100日本語版)

Updated: Aug 12, 2026

Q & A: 163 Questions and Answers

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These are following steps for registering the AI-100 exam. Step 1: Visit to Microsoft Exam Registration Step 2: Signup/Login to MICROSOFT account Step 3: Search for MICROSOFT AI-100 Certifications Exam Step 4: Select Date and Center of examination and confirm with payment value of $165

Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx

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There are various topics that you will need to study to achieve success in the Microsoft AI-100 exam. All the domains included in this certification tests are as follows:

The first subject area that you will have to master is called Analyzing Solution Requirements. It makes up 25-30% of the exam content and consists of the following subdomains: recommending Azure Cognitive Services APIs to satisfy business requirements; mapping security requirements to tools, technologies, as well as processes; selecting the software, services, and storage necessary for supporting a solution.

The next section that you have to study is Designing an AI Solution that constitutes about 40-45% of the exam questions. It revolves around the subtopics, such as designing solutions that comprise one or more pipelines; designing solutions that utilize Cognitive Services; designing solutions that implement the Microsoft Bot Framework; designing the compute infrastructure to support a solution; designing for data governance, compliance, integrity, as well as security.

The last domain included in the Microsoft AI-100 exam is titled Implementing & Monitoring AI Solutions. It makes up the remaining 25-30% of the whole content. The subtopics covered here are the following: implementing an AI workflow; integrating AI services & solution elements; monitoring and assessing the AI environment.

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Microsoft AI-100日本語 Exam Syllabus Topics:

SectionObjectives
Implement Knowledge Mining and Search Solutions- Azure Cognitive Search
  • 1. Indexing and querying data
    • 2. Skillsets and enrichment pipeline
      Implement Computer Vision Solutions- Image analysis and processing
      • 1. Computer Vision API usage
        • 2. Custom Vision training and deployment
          Responsible AI and Monitoring- AI ethics and governance
          • 1. Model monitoring and evaluation
            • 2. Responsible AI principles
              Implement Natural Language Processing (NLP)- Text analytics solutions
              • 1. Sentiment analysis
                • 2. Key phrase extraction
                  - Language understanding solutions
                  • 1. Entity extraction
                    • 2. Intent recognition
                      Design AI Solutions on Azure- Select appropriate Azure AI services
                      • 1. Azure Machine Learning integration
                        • 2. Azure Cognitive Services selection
                          • 3. Knowledge mining solutions
                            - Design conversational AI solutions
                            • 1. Language understanding (LUIS concepts)
                              • 2. Azure Bot Service architecture
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