Microsoft Azure AI Fundamentals : AI-900

AI-900 testking pdf
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Exam Code: AI-900

Exam Name: Microsoft Azure AI Fundamentals

Updated: Aug 20, 2026

Q & A: 336 Questions and Answers

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About Microsoft Azure AI Fundamentals : AI-900 Exam

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Learning Path

All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:

  • Microsoft Azure AI Fundamentals

    This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.

  • Get Started with Artificial Intelligence

    The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.

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Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

  • Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
  • Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.
  • Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
  • Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.

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

Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe considerations for developing AI solutions
- Describe responsible AI principles
Topic 2: Fundamental principles of machine learning on Azure15–20%- Describe automated machine learning
- Describe core concepts of machine learning
- Describe capabilities of Azure Machine Learning
- Describe machine learning pipelines
Topic 3: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Face
- Identify types of computer vision solutions
- Describe capabilities of Azure Form Recognizer
Topic 4: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
- Describe capabilities of Azure Translator
- Identify types of NLP solutions
Topic 5: Features of generative AI workloads on Azure20–25%- Describe use cases for generative AI
- Describe capabilities of Azure OpenAI Service
- Describe generative AI concepts
- Describe responsible AI practices for generative AI

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