Table of Contents
Microsoft Azure AI Fundamentals Quick Facts
The Microsoft Azure AI Fundamentals certification opens the door to understanding how artificial intelligence transforms businesses and everyday experiences. This overview will give you the insights you need to approach the exam with confidence and clarity, helping you quickly understand key concepts and domains covered.
How does the Microsoft Azure AI Fundamentals certification help you grow in the world of AI?
The Microsoft Azure AI Fundamentals certification (Exam AI-900) validates your foundational knowledge of artificial intelligence and how Microsoft Azure services support AI solutions. It is designed for anyone interested in learning the basics of AI, from computer vision and natural language processing to generative AI. Whether you are a student, a business professional, or a technical learner starting your AI journey, this certification provides the language and context to collaborate effectively with technical teams and better understand the transformative role of AI. By covering both AI principles and practical Azure tools, it equips you with the fundamentals needed to explore, discuss, and work with AI solutions in real-world scenarios.
Exam Domains Covered (Click to expand breakdown)
Exam Domain Breakdown
Domain 1: Describe Artificial Intelligence workloads and considerations (19% of the exam)
Identify features of common AI workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document processing workloads
- Identify features of generative AI workloads
Section summary: This section highlights the range of AI workloads, helping you distinguish between computer vision, natural language processing, document processing, and generative AI. You will learn how to identify everyday use cases, such as image recognition, language understanding, data organization, and creative applications like text or image generation. Each area has unique features that define its strengths and best scenarios for implementation.
The goal here is to build your ability to recognize which type of workload matches a given problem statement. This knowledge ensures that when you are presented with a scenario in the exam or in a workplace setting, you can connect the solution requirements with the correct workload type in Azure AI.
Identify guiding principles for responsible AI
- Describe considerations for fairness in an AI solution
- Describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
- Describe considerations for inclusiveness in an AI solution
- Describe considerations for transparency in an AI solution
- Describe considerations for accountability in an AI solution
Section summary: This section focuses on responsible AI and the ethical considerations guiding its development and application. You will explore how fairness, transparency, inclusiveness, and accountability all work together to ensure AI systems are designed to benefit everyone and minimize unintended risks.
It is not just about building advanced models but also about doing so in ways that protect privacy, maintain safety, and inspire trust. The exam will test your ability to recognize these guiding principles and understand how they lead to solutions that are both technically effective and socially responsible.
Domain 2: Describe fundamental principles of machine learning on Azure (19% of the exam)
Identify common machine learning techniques
- Identify regression machine learning scenarios
- Identify classification machine learning scenarios
- Identify clustering machine learning scenarios
- Identify features of deep learning techniques
- Identify features of the Transformer architecture
Section summary: This section helps you understand common machine learning techniques and their real-world uses. By differentiating between regression, classification, and clustering, you will be able to determine which approach is best suited for tasks like predicting numerical values, identifying categories, or grouping unlabeled data. The section also introduces deeper approaches including neural networks, deep learning methods, and the transformational impact of architectures such as Transformers.
These concepts give you the foundation to evaluate what technique works best for a given dataset or business problem. Instead of focusing on writing code, you will build an appreciation for how machine learning methods differ in their strengths and applications, providing you with the confidence to recognize scenarios on the exam and in real business cases.
Describe core machine learning concepts
- Identify features and labels in a dataset for machine learning
- Describe how training and validation datasets are used in machine learning
Section summary: This section brings focus to how data is structured and used within the machine learning process. You will learn how features and labels are essential for teaching models what to look for, and how splitting data into training and validation sets is critical to avoid bias and ensure useful predictions.
Building this understanding will help you recognize not only how models are trained but also why validation is necessary for trustworthy performance. This is central to the exam’s emphasis on grasping concepts at a foundational level, equipping you to identify proper approaches without diving into technical coding complexity.
Describe Azure Machine Learning capabilities
- Describe capabilities of automated machine learning
- Describe data and compute services for data science and machine learning
- Describe model management and deployment capabilities in Azure Machine Learning
Section summary: This section introduces the tools within Azure that simplify creating, training, and deploying models. Automated machine learning helps reduce complexity by automatically testing algorithms, while data and compute services provide scalable resources for experimentation and production. Model management ensures solutions remain reliable and accessible, with deployment features that allow real-world implementation.
By the end of this material, you will have a clear picture of how Azure Machine Learning supports the life cycle of a machine learning project from preparation to deployment. The exam expects you to recognize the key features of these services and understand their value for both beginners and experienced data scientists.
Domain 3: Describe features of computer vision workloads on Azure (19% of the exam)
Identify common types of computer vision solution
- Identify features of image classification solutions
- Identify features of object detection solutions
- Identify features of optical character recognition solutions
- Identify features of facial detection and facial analysis solutions
Section summary: This section focuses on computer vision and its wide range of applications. You will learn the distinctions between tasks like image classification, object detection, facial recognition and analysis, and optical character recognition. Each capability provides businesses and developers with different ways to process and interpret visual information.
These skills will help you recognize the correct computer vision feature for a given scenario. Whether for verifying identity, scanning documents, or classifying products in images, the concepts covered here prepare you to differentiate among solutions that Azure provides for vision-based workloads.
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
Section summary: This section highlights the Azure services that make computer vision solutions possible without building models from scratch. The Azure AI Vision service allows you to analyze, categorize, and extract text and information from images, while the Azure AI Face service is designed for detection and analysis, such as identifying demographics or verifying identities.
Recognizing the strengths of each service is an important part of the exam. By understanding which service to choose for each scenario, you will become comfortable identifying the right Azure solution to meet specific vision-based business needs.
Domain 4: Describe features of Natural Language Processing (NLP) workloads on Azure (19% of the exam)
Identify features of common NLP Workload Scenarios
- Identify features and uses for key phrase extraction
- Identify features and uses for entity recognition
- Identify features and uses for sentiment analysis
- Identify features and uses for language modeling
- Identify features and uses for speech recognition and synthesis
- Identify features and uses for translation
Section summary: This section introduces the practical uses of natural language processing. You will gain knowledge in how tasks like extracting key phrases, detecting sentiment, recognizing entities, translating languages, and handling speech recognition all play into understanding human language through technology.
Such knowledge will help you differentiate between language tasks and identify how Azure tools fulfill each one. On the exam and in real-world projects, understanding these use cases ensures accurate pairing of customer needs with technical NLP capabilities.
Identify Azure tools and services for NLP workloads
- Describe capabilities of the Azure AI Language service
- Describe capabilities of the Azure AI Speech service
Section summary: This section connects NLP concepts with specific Azure tools designed to implement them. The Azure AI Language service provides key capabilities like sentiment analysis, entity recognition, summarization, and translation. Meanwhile, the Azure AI Speech service addresses both speech-to-text and text-to-speech scenarios, enabling seamless voice interaction capabilities.
Together, these services simplify how you can integrate advanced NLP features into applications. Recognizing when to use Language versus Speech solutions is key to passing the exam and further helps in applying the right tools effectively in your career.
Domain 5: Describe features of generative AI workloads on Azure (24% of the exam)
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
Section summary: This section brings focus to the fast-growing field of generative AI. From generating new text and imagery to creating synthetic data, generative AI models open new possibilities across industries. You will explore the types of scenarios generative AI excels at, as well as the special ethical considerations tied to using this technology responsibly.
This awareness equips you to understand not just the power of generative AI, but also the balance needed to maintain fairness, accuracy, and trustworthiness when these solutions are deployed. Exam questions will expect you to connect scenarios to responsible applications of generative AI.
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
Section summary: This section introduces you to Azure’s specialized services that support generative AI. With Azure OpenAI Service providing access to advanced models, Azure AI Foundry offering tools and environments for building solutions, and the Model Catalog giving easy deployment options, these services put generative AI within reach of businesses and developers.
The exam expects you to know not just the names but the core capabilities of these services. By learning how they interconnect, you will gain confidence in identifying which service is best suited for each use case and how Azure empowers safe, scalable AI innovation.
Who should pursue the Microsoft Azure AI Fundamentals Certification?
The Microsoft Certified: Azure AI Fundamentals certification is designed for anyone eager to gain foundational knowledge of Artificial Intelligence (AI) and Microsoft Azure services. It caters to both technical and non-technical professionals, making it an excellent fit for students, business professionals, and entry-level tech enthusiasts.
This certification is particularly valuable if you are:
- Starting your journey in AI or cloud computing
- Exploring AI concepts without prior coding or data science experience
- Transitioning into a technology role from a non-technical background
- A leader, manager, or decision-maker who wants to speak fluently about AI solutions
- Looking to qualify for future Microsoft Azure role-based certifications
The Azure AI Fundamentals Certification serves as both a career launchpad and a professional credibility booster, showing employers and stakeholders that you can understand, explain, and recognize practical applications for AI in business and technology settings.
What job opportunities can this Azure AI Fundamentals certification open up?
While the exam is considered an entry-level credential, it can enhance your attractiveness for a wide variety of roles. Some of the most common positions that benefit from this certification include:
- Junior AI Engineer or AI Specialist
- Cloud Support Engineer (with a focus on AI workloads)
- Product Manager or Program Manager for AI-based initiatives
- Data Analyst or Research Assistant
- Technology Consultant for AI and cloud adoption
Additionally, this certification creates a strong foundation for advancing towards higher-level certifications such as Azure AI Engineer Associate or Azure Data Scientist Associate. Over time, this can pave the way for senior roles in data science, AI research, or solution architecture.
How much does the Microsoft AI-900 exam cost?
The Microsoft AI-900 exam costs $99 USD. Pricing may vary slightly depending on your region or local tax laws, but the baseline cost remains consistent globally.
One of the advantages of this certification is its affordability compared to more advanced certifications. For professionals or students on a budget, it provides excellent value by offering recognition from Microsoft without requiring a significant financial investment. Many individuals use this cost-effective entry point as a stepping stone to further Azure and AI certifications.
What is the exam code and how is the AI-900 exam structured?
The Microsoft Azure AI Fundamentals exam is officially designated as Exam AI-900. The test has a total of 60 questions and must be completed within 45 minutes.
The exam uses a variety of question formats, including:
- Multiple-choice (one correct answer)
- Multi-select (multiple correct answers)
- Scenario-based cases where you apply concepts to real-world solutions
The exam is designed to be accessible yet thorough, ensuring you demonstrate a well-rounded understanding of AI workloads, machine learning basics, and Azure AI services.
What score do I need to pass the AI-900 exam?
To pass the Microsoft Azure AI Fundamentals certification exam, you need a 700 out of 1000 scaled score. This passing threshold represents a solid grasp of the key concepts and Azure AI services outlined in the exam guide.
Importantly, Microsoft uses a compensatory scoring model. This means that while you don’t need to "pass" each individual section, your total combined score must meet or exceed the 700 benchmark. Even if you’re weaker in one domain, strong performance in others can help secure your overall passing score.
How many languages is the Microsoft AI-900 exam offered in?
The Microsoft AI-900 exam is available in 13 languages, making it accessible to a global community of learners. Available languages include:
- English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian, Arabic (Saudi Arabia), Chinese (Traditional), and Italian.
This wide availability aligns with Microsoft’s mission to democratize access to AI education. If your preferred language is not listed, you can request an additional 30 minutes of exam time as an accommodation.
How long does the Microsoft Azure AI Fundamentals exam last?
You’ll have 45 minutes of exam time to complete the 60 questions in Exam AI-900. Microsoft allows a total of around one hour including pre-test setup and instructions, but the actual test-taking time is 45 minutes.
While the timing may feel tight, the exam is designed at the fundamentals level, meaning most questions are straightforward applications of knowledge rather than in-depth problem-solving requiring long calculations. With simple time management, candidates can easily complete all questions.
What knowledge areas does the AI-900 exam focus on?
The AI-900 exam blueprint is organized into five content domains, each carrying a weight of the total exam:
-
Describe Artificial Intelligence workloads and considerations (15–20%)
- AI solution types such as computer vision, NLP, and document processing
- Principles of responsible AI such as fairness, safety, transparency, and accountability
-
Describe fundamental principles of machine learning (15–20%)
- Regression, classification, clustering, deep learning, and transformers
- Training vs validation data and key ML concepts
- Azure Machine Learning and automated ML features
-
Describe computer vision workloads on Azure (15–20%)
- Image classification, object detection, optical character recognition, facial detection
- Azure AI Vision and Face APIs
-
Describe Natural Language Processing workloads (15–20%)
- Sentiment analysis, entity recognition, speech services, key phrase extraction
- Azure AI Language and Azure AI Speech capabilities
-
Describe generative AI workloads on Azure (20–25%)
- Generative models and their responsible use cases
- Azure AI Foundry, Azure OpenAI Service, and Foundry model catalog
What is the most weighted domain in the AI-900 exam?
The single largest domain in the AI-900 exam is Describe features of generative AI workloads on Azure, comprising 20–25% of the total exam.
This reflects the increasing importance of generative AI technologies, including large language models and text-to-image generation, and Azure’s leading services like Azure OpenAI. Understanding responsible AI principles within this high-growth field is also emphasized heavily.
Are there prerequisites for the Azure AI Fundamentals certification?
There are no formal prerequisites for the AI-900 certification. This exam is open to anyone, regardless of technical background. That being said, it can be helpful if you are:
- Familiar with basic cloud concepts
- Aware of client-server applications
- Interested in exploring Microsoft Azure services for the first time
Because coding experience and advanced data science knowledge are not required, the certification is ideal for those looking to start their cloud AI journey without prior specialization.
How long is the Microsoft AI-900 certification valid?
Unlike associate or expert-level Microsoft certifications, the Azure AI Fundamentals certification does not expire. Once earned, it remains yours permanently as a highlight of your skills.
This is one of the reasons many learners start with AI-900, as its lifetime validity offers a reliable credential on your resume while still serving as a foundation to build toward other role-based certifications.
What kind of AI workloads are tested on the exam?
The AI-900 exam covers four main types of AI workloads:
- Computer vision (object detection, OCR, image classification, face analysis)
- Natural language processing (language understanding, sentiment analysis, speech recognition and synthesis, translation)
- Machine learning concepts and applications
- Generative AI workloads including text, chat, and content creation
These workloads represent the real-world AI applications that organizations deploy every day, ensuring this certification has strong workplace relevance.
Is hands-on experience needed to succeed in this exam?
Although the exam tests fundamental knowledge, having some hands-on experience with Azure AI services can give you an advantage. Exploring services such as Azure AI Vision, Azure AI Speech, Azure AI Language, and Azure Machine Learning allows you to connect theoretical learning to practical use cases.
Even without professional experience, Microsoft offers free training modules through Microsoft Learn, as well as an Azure free tier to experiment with real services. This makes developing practical familiarity with Azure AI highly achievable for anyone.
How can I get the best practice for the Microsoft AI-900 exam?
One of the most effective ways to study is by practicing real test-style questions that mirror the exam. Using resources like these top-rated Microsoft Azure AI Fundamentals practice exams will help you understand the question style and reinforce knowledge of AI workloads and Azure services.
Practice exams help you identify and fill learning gaps, improve confidence on test day, and ensure your preparation is well-structured. Many successful candidates highlight practice tests as the most crucial part of their study plan.
Can I take the AI-900 exam online from home?
Yes, Microsoft allows the AI-900 exam to be taken either online with remote proctoring or in person at a Pearson VUE or Certiport testing center.
If you choose online delivery, you’ll need a private area, reliable internet, and a webcam equipped device. For students and educators, Certiport offers an alternative scheduling path that may include additional academic benefits.
What makes this certification valuable for non-technical professionals?
The Azure AI Fundamentals certification shines because it bridges the gap between business decision-making and technical understanding. Non-technical professionals who complete the certification can:
- Engage more confidently with technical teams
- Recognize opportunities for AI in business solutions
- Gain credibility when speaking about cloud AI adoption
For managers, marketers, and executives, this certification can strengthen business strategy by ensuring they understand AI’s role in efficiency, scalability, and decision-making within a Microsoft Azure ecosystem.
Can students benefit from this certification?
Absolutely. This certification is particularly popular with students looking to enhance their resumes with an industry-recognized credential. Students may take advantage of discounted exam rates, scholarship opportunities, and hands-on Microsoft Azure resources through academic accounts.
For new graduates, this credential sets them apart by proving they not only understand cloud fundamentals, but also AI concepts and services, giving them an early edge in a job market that increasingly values AI competence.
Where can I find official resources for Microsoft Azure AI Fundamentals?
To explore additional study paths, register for the exam, or read Microsoft’s directly published guidelines, you can visit the official Microsoft Azure AI Fundamentals certification page.
This is your one-stop hub for Microsoft’s study guides, updates, training materials, and important policy details. It’s always wise to combine official documentation with practice exams and hands-on learning to ensure complete preparation.
