Microsoft : AI-900 Questions & Answers

Last Updated: Aug 10, 2026

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Requisites of Microsoft AI-900 Exam

In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:

  • AZ-200: Azure Fundamentals Assessment
  • AZ-100: Fundamentals of Azure

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

Average Salary of AI-900: Microsoft Azure AI Fundamentals Exam Certified Professionals

The average salary of an AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:

  • India - 11,42,117 INR
  • United States - 155,000 USD
  • England - 55,900 POUNDS
  • Europe - 105,000 EURO

Test AI-900 Scope of Knowledge

Overall, AI-900 exam tests the applicant's understanding of AI and ML through various topics categorized into 5 domains:

  • AI workloads and considerations (15-20%)

    The first topic of the final exam deals with AI workloads that are usually common. It includes candidates' understating various types of workloads such as predicting forecasting traits, the detection of anomalies, vision for computer systems, NLP, and conversational AI workloads. The examinees are also required to have a thorough understanding of the principles of AI that possess responsibility traits. It means that the candidate needs to be well aware of all the considerations for different solutions for AI which include fairness, reliability, security, privacy, inclusiveness, transparency facets, and finally, functions for accountability.

  • Workloads for Natural Language Processing (NLP) related to Azure (15-20%)

    The fourth portion of AI-900 exam deals with Natural Language Processing or NLP workloads in Azure. To get a passing score, the entrant must have a firm grasp on the facets and usage of diverse NLP scenarios for workloads which comprise the extraction of the key phrase, the recognition of entities, the analysis of sentiments, modeling for languages, the recognition of speech, and translation traits. The applicants are also required to know about different techniques as well as services of Azure used for various NLP workloads. They are required to be capable of identifying diverse abilities of various services such as text analysis, LUIS, speech, and translator text.

  • Fundamental principles of machine learning on Azure (30-35%)

    In the second section, the applicant's knowledge of fundamental principles of machine learning in relation to Azure cloud computing services will be tested. This requires the applicant to have a clear comprehension of all the common types of various scenarios for ML that include classification, regression, and clustering. It is also essential to have a thorough understanding of the core MI concepts such as labels & features in datasets, dataset training that go with the validation process, etc. The entrant must as well as be capable of identifying the core task required in developing an ML solution. So, understanding the ingestion and preparation of data chunks, selection and engineering of various features, evaluation procedure as well as training for models, and deployment & management of those models will be vital. Finally, this portion of AI-900 exam includes concepts of ML automated user interface that are ML Wizard UI and Azure ML designer.

  • Workloads for computer vision based on Azure and features (15-20%)

    This segment of the official test is all about workloads for computer vision in relation to Azure services. To get through this part, one must have a general grasp of the various common forms of computer vision solutions that include image classification, object detection, semantic segmentation, recognition for the optical character, and facial detection. The applicants are also required to have an understanding of the diverse methods including services used in Azure for activities that utilize computer vision. Such methods and services include the vision for computers, vision for custom, services for face vision, and at last, the recognizer of the form.

  • Conversational workloads for AI based on Azure and features (15-20%)

    The fifth and final domain for such an exam tests the entrant's understanding of conversational AI workloads. This requires the comprehension of facets as well as cases for the usage of conversational traits for AI which includes web chatbots, voice menus for the telephone, and digital assistants used for personal purposes. The applicant also needs to know how to check various Azure services related to conversational AI. Additionally, they should have a firm understanding of the QnA Maker Service & Bot service. Finally, it is essential for candidates to know and be able to describe the common characteristics of conversational artificial intelligence solutions.

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

As for this area, it includes the following:

  • 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.
  • 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.
  • 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.
  • 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.

Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Describe Azure capabilities for NLP
- Identify common NLP tasks
- Identify Azure AI services for NLP
Describe features of Generative AI workloads on Azure15-20%- Describe generative AI concepts
- Describe Azure OpenAI Service capabilities
- Identify responsible AI considerations for generative AI
Describe AI workloads and considerations15-20%- Identify features of common AI workloads
- Identify guiding principles for responsible AI
Describe fundamental principles of machine learning on Azure30-35%- Describe core machine learning concepts
- Describe Azure Machine Learning capabilities
- Describe features of no-code automated ML
- Identify common machine learning tasks
Describe features of computer vision workloads on Azure15-20%- Identify common computer vision tasks
- Identify Azure AI services for computer vision
- Describe Azure capabilities for computer vision
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