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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.

Explain Computer Vision Workloads Features on Azure (15-20%)

Here, the following skills will be measured:

  • Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
  • Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.

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Microsoft AI-900 Exam Syllabus Topics:

TopicDetails

Describe Artificial Intelligence workloads and considerations (20-25%)

Identify features of common AI workloads- identify features of anomaly detection workloads
- identify computer vision workloads
- identify natural language processing workloads
- identify knowledge mining workloads
Identify guiding principles for responsible AI- describe considerations for fairness in an AI solution
- describe considerations for reliability and safety in an AI solution
- describe considerations for privacy and security in an AI solution
- describe considerations for inclusiveness in an AI solution
- describe considerations for transparency in an AI solution
- describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (25-30%)

Identify common machine learning types- identify regression machine learning scenarios
- identify classification machine learning scenarios
- identify clustering machine learning scenarios
Describe core machine learning concepts- identify features and labels in a dataset for machine learning
- describe how training and validation datasets are used in machine learning
Describe capabilities of visual tools in Azure Machine Learning studio- automated machine learning
- azure Machine Learning designer

Describe features of computer vision workloads on Azure (15-20%)

Identify common types of computer vision solution- identify features of image classification solutions
- identify features of object detection solutions
- identify features of optical character recognition solutions
- identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks- identify capabilities of the Computer Vision service
- identify capabilities of the Custom Vision service
- identify capabilities of the Face service
- identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)

Identify features of common NLP Workload Scenarios- identify features and uses for key phrase extraction
- identify features and uses for entity recognition
- identify features and uses for sentiment analysis
- identify features and uses for language modeling
- identify features and uses for speech recognition and synthesis
- identify features and uses for translation
Identify Azure tools and services for NLP workloads- identify capabilities of the Language service
- identify capabilities of the Speech service
- identify capabilities of the Translator service
Identify considerations for conversational AI solutions on Azure- identify features and uses for bots
- identify capabilities of the Azure Bot service

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

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