What is the Microsoft AI-900 Exam?
The AI-900: Microsoft Azure AI Fundamentals exam is an entry-level certification that introduces you to artificial intelligence (AI) and machine learning (ML) concepts. It focuses on how Microsoft Azure provides AI solutions through its cloud services. This exam is designed to validate your basic understanding of AI workloads, fundamental principles, and how Azure makes it easy to apply AI in real-world scenarios.
It’s not about coding or deep technical expertise; it’s about knowing the foundations of AI and how they fit into Microsoft’s ecosystem.
Who should take this exam
The AI-900 is ideal for:
- Technical learners who want to build a strong foundation before moving to advanced Azure AI certifications.
- Beginners in AI and Cloud who want to understand how AI works without needing advanced math or programming knowledge.
- Business professionals and non-technical roles (like sales, marketing, or product management) who want to see how AI can help their organizations.
- Students or career changers exploring a career path in AI or data.
Prerequisites and recommendations
- No strict prerequisites – anyone can take AI-900.
- Recommended: Basic knowledge of cloud computing concepts (like what the cloud is and why businesses use it).
- An interest in AI and how it solves problems will make learning easier.
- Experience with Microsoft Azure isn’t required but helps in understanding services quickly.
Exam objectives and domains
The AI-900 exam is divided into several domains (knowledge areas):
- AI Workloads and Considerations – 15–20%
- Fundamentals of Machine Learning (ML) – 30–35%
- Features of Computer Vision Workloads – 15–20%
- Features of Natural Language Processing (NLP) Workloads – 15–20%
- Features of Conversational AI Workloads – 15–20%
Objective details by domain
| Domain | Key Topics | Weightage |
|---|---|---|
| 1. AI Workloads and Considerations | What AI is, difference from traditional software, Responsible AI (fairness, transparency, privacy, security), real-world AI use cases (chatbots, recommendations, fraud detection). | 15–20% |
| 2. Machine Learning Fundamentals | ML basics, training/validation/testing, supervised vs. unsupervised vs. reinforcement learning, Azure ML automation & deployment. | 30–35% |
| 3. Computer Vision | Image recognition, object detection, facial recognition, Azure Computer Vision API, Custom Vision, Face API, real-life applications (quality control, medical imaging, security). | 15–20% |
| 4. Natural Language Processing (NLP) | Text analytics, translation, sentiment analysis, Azure services (Text Analytics, Translator, LUIS), applications in chatbots & voice assistants. | 15–20% |
| 5. Conversational AI | Building chatbots, Azure Bot Service, NLP integration, scenarios like customer support bots and virtual agents. | 15–20% |
1. AI Workloads and Considerations
- Understand what AI is and how it differs from traditional software.
- Learn about responsible AI, fairness, transparency, privacy, and security.
- Explore real-world AI applications like chatbots, recommendation systems, and fraud detection.
2. Machine Learning Fundamentals
- What machine learning is and how it works.
- Core concepts: training, validation, testing, and evaluating models.
- Introduction to supervised, unsupervised, and reinforcement learning.
- How Azure Machine Learning helps automate and deploy ML solutions.
3. Computer Vision
- Basics of image recognition, object detection, and facial recognition.
- Using Azure services like Computer Vision API, Custom Vision, and Face API.
- Real-life use cases: quality control, medical imaging, security, etc.
4. Natural Language Processing (NLP)
- Understanding text analytics, translation, and sentiment analysis.
- Azure services like Text Analytics, Translator, and Language Understanding (LUIS).
- How NLP is used in chatbots, customer service, and voice assistants.
5. Conversational AI
- Practical scenarios like customer support bots and virtual agents
- Basics of building chatbots.
- Microsoft Azure Bot Service and integration with NLP.
What changed in this version
Microsoft regularly updates the AI-900 exam to reflect the latest Azure services and AI capabilities. Recent changes include:
- Slight adjustments in weightage of NLP and conversational AI.
- Updated tools in Azure Machine Learning Studio.
- Expanded focus on responsible AI principles.
Registration and scheduling
- You can register through the Microsoft Certification website.
- The exam is available online (proctored at home) or at a test center (via Pearson VUE).
- Choose the format that best suits your comfort.
Pricing and vouchers
- The exam typically costs $99 USD (prices may vary by country).
- Microsoft sometimes offers student discounts (50% off).
- Companies or training providers may give exam vouchers that cover the cost.
Policies you should know
- You need valid ID (passport, license, etc.) on exam day.
- Rescheduling is allowed, but must be done at least 24 hours in advance.
- If you fail, Microsoft’s retake policy requires a waiting period before trying again.
Scoring and results
- Scores range from 100 to 1000.
- Passing score: 700.
- You get results immediately after the exam.
- A detailed score report shows strengths and weaknesses by domain.
Exam day and test experience
- If online: Ensure a stable internet connection, a quiet room, and a webcam.
- The proctor will verify your ID and surroundings.
- Expect around 40–60 questions (multiple-choice, drag-and-drop, case studies).
- Time: 60 minutes (plus extra for check-in).
Study plan and resources
Week 1 – Foundations & AI Basics
Goal: Understand AI concepts and Azure AI services at a high level.
- Day 1–2: Introduction to AI
- What AI is, AI vs. traditional software.
- Real-world AI applications (chatbots, recommendations, fraud detection).
- Responsible AI principles: fairness, privacy, transparency.
- Day 3–4: Azure AI Workloads
- Explore Azure services overview (Cognitive Services, Machine Learning, Bot Service).
- Microsoft Learn modules: Introduction to AI-900 (free).
- Day 5–6: Machine Learning Fundamentals
- Learn what supervised, unsupervised, and reinforcement learning mean.
- Training, validation, and evaluation concepts.
- Get hands-on in Azure Machine Learning Studio (play with a sample dataset).
- Day 7 (Weekend): Review + Practice Quiz
- Revise all AI basics.
- Do a short practice test (Microsoft Learn knowledge checks or free quizzes online).
Week 2 – AI Workloads in Depth
Goal: Get deeper into computer vision, NLP, and conversational AI.
- Day 8–9: Computer Vision
- Understand image classification, object detection, and facial recognition.
- Explore Azure Computer Vision, Custom Vision, and Face API.
- Try uploading a sample image in Azure Vision demo.
- Day 10–11: Natural Language Processing (NLP)
- Basics: sentiment analysis, key phrase extraction, translation.
- Azure services: Text Analytics, Translator, LUIS (Language Understanding).
- Practice with sample text using Azure demo tools.
- Day 12–13: Conversational AI
- Learn chatbot basics and Azure Bot Service.
- Understand how chatbots integrate with NLP.
- Build a simple test bot in Microsoft Bot Framework demo.
- Day 14 (Weekend): Review + Practice Quiz
- Go through all domains studied this week.
- Take another practice test (20–30 questions).
- Note down weak areas for Week 3.
Week 3 – Exam Prep & Mock Testing
Goal: Strengthen weak areas and get exam-ready.
- Day 15–16: Target Weak Areas
- Review your mistakes from last week’s quizzes.
- Revisit Microsoft Learn modules for those topics.
- Day 17–18: Full Practice Tests
- Take a timed practice exam (40–60 questions).
- Review every answer, even the ones you got right, to reinforce knowledge.
- Use Cert Mage exam dumps for real-style questions.
- Day 19–20: Quick Revision
- Summarize all domains: AI workloads, ML basics, computer vision, NLP, conversational AI.
- Create quick flashcards for key terms (supervised vs. unsupervised, responsible AI, services).
- Day 21 (Final Day): Light Review + Rest
- Skim through notes, flashcards, and Microsoft Learn summaries.
- Relax and sleep well before the exam day.
Preparing with the right resources makes all the difference. For the best AI-900 exam dumps and practice questions, check out Cert Empire. The updated and reliable exam material helps you practice real exam-style questions, understand concepts better, and boost your chances of passing on the first try.
Certification validity and renewal
AI-900 is valid for 1 year.
Renewal is free and can be done online through Microsoft Learn by passing a short assessment.
Career outcomes
Earning AI-900 doesn’t make you an AI engineer right away, but it:
- Builds a foundation for advanced certifications and roles.
- Shows employers you understand AI basics.
- Helps you stand out in entry-level jobs in cloud, AI, or business intelligence.
| Job Role | What You Do | Average Salary (USD) |
|---|---|---|
| AI/ML Support Engineer | Assist in deploying and troubleshooting AI models and Azure AI services. | $60k–$85k |
| Data Analyst (Entry-Level) | Work with data insights, basic ML models, and visualization tools. | $55k–$80k |
| Cloud Support Associate | Support Azure cloud and AI workloads for clients. | $50k–$75k |
| AI Business Analyst | Bridge the gap between business needs and AI solutions. | $65k–$90k |
| Junior AI Developer | Work on chatbots, NLP, and small-scale AI projects. | $65k–$95k |
Related or next-step certifications
After AI-900, you can explore:
- DP-100: Designing and Implementing Data Science Solutions on Azure (for data scientists).
- DP-900: Azure Data Fundamentals (data-focused).
- AZ-900: Azure Fundamentals (cloud-focused).
- AI-102: Designing and Implementing Azure AI Solutions (advanced AI).
How this exam compares to similar certifications
- AI-900 vs. AZ-900: Both are fundamentals, but AI-900 is about AI concepts, while AZ-900 is general cloud.
- AI-900 vs. Google Cloud AI Fundamentals: Similar in scope, but Microsoft AI-900 is more structured and Azure-specific.
- AI-900 vs. IBM AI Engineering Certificate: IBM focuses on technical skills, AI-900 stays non-technical and beginner-friendly.
Frequently Asked Questions (FAQs)
Who should take the AI-900 exam?
This exam is ideal for students, business professionals, career changers, or anyone curious about AI and Microsoft Azure. It’s especially useful if you want to build a foundation before moving to advanced AI or cloud certifications.
Do I need coding skills for AI-900?
No, coding or advanced math knowledge is not required. The exam focuses on understanding AI concepts and Azure services, not programming.
How much does the AI-900 exam cost?
The standard price is $99 USD, but students may get up to 50% discount. Prices can vary depending on your country or testing provider.
How long should I study for the AI-900 exam?
On average, 2–3 weeks of preparation (1–2 hours per day) is enough for most candidates. A one-week crash study plan is also possible with focused learning.
How many questions are on the AI-900 exam?
You can expect around 40–60 questions in multiple-choice and scenario-based formats.
What is the passing score for AI-900?
You need a score of 700 out of 1000 to pass the exam.
How long is the AI-900 exam valid?
The certification is valid for 1 year. After that, you can renew it for free through a short online assessment on Microsoft Learn.
Can AI-900 help me get a job?
Yes, AI-900 strengthens your resume for entry-level roles like AI Support Engineer, Data Analyst, or Cloud Support Associate. While it won’t make you an AI engineer alone, it sets a strong foundation for future career growth.
What’s the next step after AI-900?
After completing AI-900, you can move to more advanced certifications such as AI-102 (Azure AI Engineer Associate), DP-100 (Data Scientist), or AZ-900 (Azure Fundamentals) to expand your expertise.
