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How much AZ-900:Microsoft Azure AI Fundamentals Exam Cost

The exam cost of Microsoft Azure AI Fundamentals is 99 USD.

Prerequisites

Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.

The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.

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

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Microsoft AI-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Training and validation concepts
    • 2. Supervised vs unsupervised learning
      Topic 2: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
      • 1. Common AI workloads
        • 2. Responsible AI principles
          Topic 3: 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
                Topic 4: Features of computer vision workloads on Azure15-20%- Computer vision solutions
                • 1. Object detection
                  • 2. OCR and image analysis
                    • 3. Image classification

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