Microsoft Azure AI Fundamentals : AI-900

  • Exam Code: AI-900
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Aug 29, 2026     Q & A: 336 Questions and Answers

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About Microsoft AI-900 Exam

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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

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How much AZ-900:Microsoft Azure AI Fundamentals Exam Cost

The exam cost of Microsoft Azure AI Fundamentals is 99 USD.

For more info read reference:

Microsoft AI-900 Official Certification Learning Site

Training and Certification Guide

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Difficulty in Attempting AI-900: Microsoft Azure AI Fundamentals Exam

Exam difficulty is not difficult and the material available in the Microsoft Learning website is sufficient to pass the exam easily with high score. The learning method needs to be fast. Otherwise, the score will not be high. The preparation material, including pre-exam certification, are used for time management. Use the exam simulator for practice. Performance requirements are quick response time. Translation of the data is required for this purpose. Updated and reliable information about the exam is required for this purpose. Real data is used for testing and certification. Engineering requirements are varied. Shopping website is used to buy the bag for this purpose. Microsoft AI-900 exam dumps are used in the exam. Relevant terminology used in AI-900:Microsoft Azure AI Fundamentals Exam is explained below. Metrics are used for this purpose. One of the metrics is accuracy. Basic tasks are performed for this purpose. Real-time analytics is used for this purpose. The architecture is required for the Microsoft Azure AI Fundamentals Exam.

Detecting anomalies in the data can help to avoid some mistakes. The purpose is to use the machine learning algorithms to solve problems. Syllabus for the Microsoft AI-900 exam is the correct use of the data for this service. Vendor links are used for this purpose. The platform is used to get the information from the Microsoft Azure AI Fundamentals Exam. Percentage of the data is used for this purpose. Validation and test is required for this purpose. Fairness is used for this purpose. Matrix factorization is used for the training.

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Microsoft AI-900 Exam Syllabus Topics:

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

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