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Important facts you need to know about AI-900: Microsoft Azure AI Fundamentals Exam

Preparing for the exam will help you screen out questions that are irrelevant for this certification. Ingestion of the data is the most important role in AI. Ready to use the data as soon as possible. Anomaly detection refers to all situations where something out of the ordinary is happening. Accountability models are used for the accuracy rate. Transparency refers to using the data for this service. Interoperability is used on the platform. Microsoft AI-900 exam dumps in order to get the best scores with the Microsoft Azure AI Fundamentals Exam. Tech terms used in AZ-900:Microsoft Azure AI Fundamentals Exam. Serviceidentify is used in the process of AI. Files are used to store the data. The AI application is the product of the AI. Learning is used for this purpose to provide better accuracy rate.

Concepts of the AI are explained in the Microsoft AI-900 exam. Image classification is used as the labeling. Recommendation engine is used as the indexing. Intelligent chatbots are used as the chatbot. Community engagement is the chatbot. Custom bot is used as the chatbot. Extracts are used for this purpose. Brainpool is the tool used to perform the extraction. Word embedding is used as the vector training. Dimensionality reduction is used for this purpose. Intelligent chatbot are used as the chatbot.

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Preparation Process

Microsoft offers a wide range of resources to help you prepare for your certification exam. The first step in the preparation process is to go through the topics in detail. This will enable you to understand the specific areas that are covered in the test. With this understanding, you can start to get the materials that will provide more knowledge on the objectives. Thus, Microsoft comes with two training options to help the candidates develop the required skills. These are free online training and instructor-led training.

  • Practice Test: As a future examinee, you also have access to the official practice test to evaluate your understanding of the exam topics before taking the actual test. The link to this training tool can be found on the Microsoft website.
  • Free Online Training: The individuals can explore five free training tutorials that are available on the official exam webpage. They have a series of modules that focus on the specific domains of the test. These training tutorials are recommended for those candidates who are comfortable with the self-paced study. The topics of these manuals include the following options:
    • Get Started with AI on Azure;
    • Create No-Code Predictive Models with Azure ML;
    • Explore NLP;
    • Explore Computer Vision on Microsoft Azure;
    • Explore Conversational Artificial Intelligence.

    The students can find the details of these learning paths on the official website.

  • Instructor-Led Training: The Microsoft Azure AI Fundamentals course is designed to help you gain the required skills to pass the exam and obtain the certification. It introduces the learners to the fundamental concepts of AI and the services available in Microsoft Azure that are useful in creating AI solutions. This training is delivered by the certified partners and its details can be found on the Microsoft website. It is important to mention that this course is designed to be combined with other online materials that are available on the Microsoft Learning platform.

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

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Microsoft AI-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
  • 1. Language modeling and translation
    • 2. Sentiment analysis
      • 3. Key phrase extraction
        Features of computer vision workloads on Azure15-20%- Computer vision solutions
        • 1. Image classification
          • 2. OCR and image analysis
            • 3. Object detection
              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

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