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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.
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How to Prepare For AI-900: Microsoft Azure AI Fundamentals
Preparation Guide for AI-900: Microsoft Azure AI Fundamentals
Introduction
Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. The objective of the certification is to verify that approved partners have the knowledge and skills to deploy, configure, set up, activate, maintain, troubleshoot, and support all necessary Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam taker level of competence in the functions, features, and services of Azure. The Microsoft AI-900 Certification Exam is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017, and NET Framework. Courses are available online or in-person depending on individual needs.
This article will cover the areas of knowledge required for this exam, how many questions are covered in each area, and who is taking this certification exam.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Certification Topics of Microsoft AI-900 Exam
Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.
- Describe features of conversational AI workloads on Azure (15-20%)
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Describe features of computer vision workloads on Azure (15-20%)
- Describe fundamental principles of machine learning on Azure (30-35%)
- Describe AI workloads and considerations (15-20%)
Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe responsible AI practices for generative AI |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Identify types of NLP solutions |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |




