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Last Updated: Sep 13, 2026

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Certification Path

The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.

Career Prospects

Although the Microsoft AI-900 exam is associated with the foundational-level certification, all the successful candidates can pursue various lucrative career paths in the field of IT. Some of the job roles that these professionals can work in include a Data Scientist, a Software Developer, and an Azure AI Engineer, among others. They can also proceed to get some other role-based certificates to boost their potential in the world of Information Technology. With the Microsoft Certified: Azure AI Fundamentals certification, you can earn an average of $48,000 per annum as an entry-level specialist. Those with some work experience can get an average of $74,000 per year.

Microsoft AI-900 Practice Test Questions, Microsoft AI-900 Exam Practice Test Questions

The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.

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Studying with Revision Books

Apart from the training endorsed by the certification vendor, it’s a good idea to exhaust the study guides from Amazon. Thus, here’s what you need to know about the two must-have revision books for the Microsoft AI-900 certification exam prep:

  • Microsoft AI MVP Book: Practical Guide to Microsoft AI Written by 17 AI and Azure MVPs from all around the world

    This book is a collection of skills and knowledge from a team of AI and Azure specialists with countless years of experience in the field. It is written by Microsoft Azure MVPs including Leila Etaati, Anupama Natarajan, Ashraf Ghonaim, Cameron Vetter, David Rendon, etc. who used their cumulative years of experience to design a definitive study guide and a solution for all trainees preparing for AI-900 exam. The authors state it categorically that this book does not intend to cover everything there’s to know about Microsoft AI. And that it is important to combine it with other study materials to have a better chance of passing the Microsoft AI-900 exam. In a nutshell, this guide targets AI and ML developers, architects, data scientists, .net developers, and consultants who are already familiar with the foundational AI concepts. Remember it is not your best guide if you are starting from a zero, but an excellent choice if you are already familiar with most of the concepts in this field.

  • Azure AI Fundamentals: Study Guide and Practice Exam for Microsoft AI-900 Exam

    This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.

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

Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
  • 1. Responsible AI principles
    • 2. Common AI workloads
      Features of computer vision workloads on Azure15-20%- Computer vision solutions
      • 1. OCR and image analysis
        • 2. Object detection
          • 3. Image classification
            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. Sentiment analysis
                  • 2. Language modeling and translation
                    • 3. Key phrase extraction
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