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Last Updated: Aug 14, 2026

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Exam Topics

The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:

Describe AI Considerations and Workloads (15-20%)

The first topic evaluates the following skills:

  • Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.
  • Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.

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

As for this area, it includes the following:

  • 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.
  • 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.
  • 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.
  • 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.

Objective of Microsoft AI-900 Exam

It's important to note that the Microsoft AI-900 exam isn't an exam on Azure; the objective of the exam is to make sure you know how to build and manage intelligent cloud solutions and services on Azure. Foundation to Azure, or you can still get the certificate. Defined best practices for solution design, deployment, and administration. Identity of Azure services. Conclude testing by connecting the solution to the infrastructure to test for functionality. Instructor for the exam has extensive experience with Azure. Textidentify Azure services. Define identity of Azure services. Rewarding in the form of free exam vouchers and in-demand credential. Microsoft AI-900 exam dumps prepare you to become qualified Azure solutions specialist. Focus on the skills that are of high value in your organization. Bots are an important part of this exam.

Pay special attention to Cloud design principles. Terms used to describe the Azure platform. Standing out in the crowd and getting the job you want. Regular updates by Microsoft experts. Space for comments with every question. Sources for determining Azure pricing. Document your approach to meeting your goals. Positive impact of the exam on your career path.

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

The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.

Microsoft AI-900 Korean Exam Syllabus Topics:

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

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