Exam AI-900: Microsoft Azure AI Fundamentals

The AI-900: Microsoft Azure AI Fundamentals certification offers individuals an excellent opportunity to validate their foundational understanding of artificial intelligence (AI), machine learning (ML), and the core Microsoft Azure services that support these technologies. This credential is ideal for those looking to explore how AI workloads are implemented using Microsoft Azure, even without a deep technical background.

Who Should Consider This Certification?

This certification is designed for a broad audience, including individuals from both technical and non-technical backgrounds. Whether you are a business professional, student, or aspiring technologist, the AI-900 exam helps you build and showcase essential knowledge of AI and ML, without the need for prior data science or software engineering experience.

However, candidates will benefit from having a basic understanding of:

Core cloud computing concepts
Client-server architecture and application workflows

Familiarity with these foundational ideas will support a smoother learning experience, especially when exploring how Azure enables intelligent solutions.

Learning Pathways and Preparation

To prepare effectively for the AI-900 exam, candidates are encouraged to engage with either self-paced modules available on Microsoft Learn or instructor-led training courses. These resources are structured to guide learners through key concepts such as responsible AI principles, fundamental ML models, natural language processing (NLP), computer vision, and conversational AI.

Career Progression and Next Steps

While the Azure AI Fundamentals certification is not a mandatory prerequisite for more advanced Azure certifications, it serves as a valuable stepping stone. It lays the groundwork for role-based certifications, including:

Azure Data Scientist Associate
Azure AI Engineer Associate

Completing the AI-900 exam can therefore be a strategic first move for individuals looking to pursue a specialized career in AI and data on the Azure platform.

Exam Details

The AI-900: Microsoft Azure AI Fundamentals exam is designed for individuals at the beginner level, particularly those aspiring to pursue roles such as AI Engineer. This proctored exam has a total duration of 45 minutes and must be completed without access to books or reference materials. Candidates may encounter interactive components during the assessment, requiring practical engagement with AI concepts and Azure services.

The exam is available in multiple languages, including English, Japanese, Simplified Chinese, Korean, German, French, Spanish, Brazilian Portuguese, Russian, Indonesian, Arabic (Saudi Arabia), Traditional Chinese, and Italian, making it accessible to a global audience. To successfully pass the exam, candidates must achieve a minimum score of 700. Microsoft provides accommodations for candidates who use assistive technologies, require additional time, or need modifications to any part of the testing process. These accommodations can be requested in advance to ensure a fair and inclusive exam experience.

Course Outline

The exam covers the following topics:

Topic 1: Describing Artificial Intelligence workloads and considerations (15-20%)

1.1 Identify features of common AI workloads

Identify computer vision workloads (Microsoft Documentation: Applying content tags to images, Detect common objects in images, Detect popular brands in images)
Identifying natural language processing workloads (Microsoft Documentation: Choosing a natural language processing technology in Azure)
Identify document intelligence workloads
Identify features of generative AI workloads

1.2 Identify guiding principles for responsible AI

describing the considerations for fairness in an AI solution (Microsoft Documentation: Model performance and fairness (preview))
explaining the considerations for reliability and safety in an AI solution (Microsoft Documentation: Responsible and trusted AI)
describing the considerations for privacy and security in an AI solution (Microsoft Documentation: Responsible AI)
explaining the considerations for inclusiveness in an AI solution (Microsoft Documentation: Responsible and trusted AI)
describing considerations for transparency in an AI solution (Microsoft Documentation: Identify guiding principles for responsible AI)
describing considerations for accountability in an AI solution (Microsoft Documentation: Responsible and trusted AI, Identify guiding principles for responsible AI)

Topic 2: Understand the fundamental principles of machine learning on Azure (15-20%)

2.1 Identify common machine learning techniques

identifying regression machine learning scenarios (Microsoft Documentation: Linear Regression)
identifying classification machine learning scenarios (Microsoft Documentation: Classification modules)
identify clustering machine learning scenarios (Microsoft Documentation: Clustering modules)
Identify features of deep learning techniques
Identify features of the Transformer architecture

2.2 Describe core machine learning concepts

identifying features and labels in a dataset for machine learning (Microsoft Documentation: Create and explore Azure Machine Learning dataset with labels)
explaining how training and validation datasets are used in machine learning (Microsoft Documentation: Configure training, validation, cross-validation and test data in automated machine learning)

2.3 Describe Azure Machine Learning capabilities

Describe capabilities of Automated machine learning (Microsoft Documentation: Automated machine learning (AutoML))
Describe data and compute services for data science and machine learning
Describe model management and deployment capabilities in Azure Machine Learning

Topic 3: Learn about the features of computer vision workloads on Azure (15-20%)

3.1 Identify common types of computer vision solution:

identifying features of image classification solutions (Microsoft Documentation: Train image classification models with MNIST data and scikit-learn)
identify features of object detection solutions (Microsoft Documentation: Detect common objects in images)
identifying features of optical character recognition solutions (Microsoft Documentation: Optical Character Recognition (OCR))
identify features of facial detection and facial analysis solutions

3.2 Identify Azure tools and services for computer vision tasks

Describe capabilities of the Azure AI Vision service
Describe capabilities of the Azure AI Face detection service

Topic 4: Describing features of Natural Language Processing (NLP) workloads on Azure (15-20%)

4.1 Identify features of common NLP Workload Scenarios

identifying features and uses for key phrase extraction (Microsoft Documentation: How to extract key phrases using Text Analytics)
identify the features and uses for entity recognition (Microsoft Documentation: Entity Recognition cognitive skill)
identifying features and uses for sentiment analysis (Microsoft Documentation: What is sentiment analysis and opinion mining)
identifying features and uses for language modeling (Microsoft Documentation: language detection in Azure Cognitive Service for Language)
identify the features and uses for speech recognition and synthesis (Microsoft Documentation: Get started with speech-to-text, Speech service)
identifying features and uses for translation (Microsoft Documentation: Translator service)

4.2 Identify Azure tools and services for NLP workloads

identifying capabilities of the Azure AI Language Service (Microsoft Documentation: Azure Cognitive Service for Language)
identify the capabilities of the Azure AI Speech service (Microsoft Documentation: Speech service)

Topic 5: Understand features of generative AI workloads on Azure (15–20%)

5.1 Identify features of generative AI solutions

Identify features of generative AI models
Identify common scenarios for generative AI
Identify responsible AI considerations for generative AI

5.2 Identify generative AI services and capabilities in Microsoft Azure

Describe features and capabilities of Azure AI Foundry
Describe features and capabilities of Azure OpenAI service
Describe features and capabilities of Azure AI Foundry model catalog

Microsoft AI-900 Exam FAQs

Click Here for FAQs!

Microsoft Certification Exam Policies

Microsoft upholds a comprehensive set of certification exam policies designed to maintain the credibility, fairness, and integrity of its global certification program. These policies ensure a consistent and secure testing experience for all candidates, whether they choose to take their exams remotely or at an authorized testing center. By following these guidelines, candidates contribute to preserving the professional reputation and industry recognition of Microsoft certifications.

Exam Retake Policy

Candidates who do not pass a Microsoft certification exam on their first attempt must observe a mandatory 24-hour waiting period before trying again. For all subsequent attempts—from the second through the fifth—a 14-day interval between each attempt is required. To uphold exam standards and limit excessive retakes, Microsoft imposes a cap of five exam attempts within a 12-month period, starting from the date of the initial test.

Rescheduling and Cancellation Policy

Microsoft allows candidates to reschedule or cancel exams with minimal restrictions if done in advance. Changes made six or more business days before the scheduled exam date are free of charge. However, any adjustments made within five business days of the appointment are subject to a rescheduling or cancellation fee. If a candidate misses the exam or cancels less than 24 hours before the scheduled time, the exam fee will be forfeited. In cases involving emergencies or verified accessibility needs, Microsoft may grant exceptions or accommodations upon request and documentation.

Microsoft AI-900 Exam Study Guide

1. Understand the Exam Objective

A clear understanding of the exam objectives is essential for successfully preparing for the AI-900: Microsoft Azure AI Fundamentals certification. The exam is designed to assess foundational knowledge of artificial intelligence (AI) and machine learning (ML), along with the ability to identify how these concepts are implemented using Microsoft Azure services. By thoroughly reviewing the official exam skills outline, candidates can align their study efforts with the key topic areas, such as AI workloads, responsible AI, natural language processing, computer vision, and conversational AI. Focusing on these core objectives not only enhances exam readiness but also builds a solid foundation for future learning and career advancement in AI-related roles.

2. Explore the Exam Learning Paths

To effectively prepare for the AI-900: Microsoft Azure AI Fundamentals exam, it’s important to explore the structured learning paths provided by Microsoft and other trusted platforms. These learning paths are designed to guide candidates through essential topics such as AI principles, machine learning models, and Azure-based AI solutions. Whether you prefer self-paced modules or instructor-led training, the learning resources are organized to match the official exam objectives, helping you build knowledge step by step. By following these curated paths, you can develop a deeper understanding of both the theoretical and practical aspects of AI, making your exam preparation more efficient and targeted. The modules are:

3. Leverage Microsoft Documentation for In-Depth Exam Preparation

For a comprehensive and accurate understanding of topics covered in the AI-900: Microsoft Azure AI Fundamentals exam, referring to official Microsoft documentation is highly recommended. These resources offer detailed explanations, real-world use cases, and up-to-date information on Azure AI services and tools. Microsoft’s documentation can help clarify complex concepts such as cognitive services, machine learning, and responsible AI, which are key components of the exam. Integrating these references into your study plan not only reinforces your learning but also ensures that your knowledge aligns with the latest Azure technologies and best practices.

4. Join Study Groups for Collaborative AI-900 Exam Preparation

Participating in study groups can significantly enhance your preparation for the AI-900: Microsoft Azure AI Fundamentals exam. These groups offer a collaborative environment where you can discuss key topics, clarify doubts, and gain insights from peers who are also preparing for the exam. Engaging with a community allows you to share resources, stay motivated, and learn from different perspectives and real-world experiences. Whether through online forums, social media groups, or virtual meetups, study groups can provide the support and accountability needed to reinforce your understanding and boost your confidence before exam day.

5. Take AI-900 Practice Tests to Assess Your Readiness

Incorporating practice tests into your study routine is a valuable strategy for preparing for the AI-900: Microsoft Azure AI Fundamentals exam. These mock exams simulate the actual test environment, helping you become familiar with the question format, time constraints, and topic distribution. Practice tests also allow you to identify areas where you need improvement, enabling focused revision and better time management. By regularly evaluating your progress through these assessments, you can build confidence, reinforce key concepts, and increase your chances of success on the actual exam.