Microsoft · AI-900
Validates foundational knowledge of machine learning and AI concepts and related Microsoft Azure services. Designed for candidates with both technical and non-technical backgrounds.
Practice Questions
464
≈ 9 practice exams
Duration
45 minutes
Passing Score
700/1000
Difficulty
FundamentalsLast Updated
Jan 2025
Use this AI-900 practice exam to prepare for Microsoft Certified: Azure AI Fundamentals (AI-900) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 464 questions for Microsoft AI-900, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Artificial Intelligence workloads and considerations, Machine learning principles on Azure, Computer vision workloads on Azure, Natural Language Processing workloads on Azure, and Generative AI workloads on Azure. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The Microsoft Certified: Azure AI Fundamentals certification (AI-900) validates foundational knowledge of machine learning and artificial intelligence concepts, along with the Microsoft Azure services that support them. It covers the full breadth of modern AI workloads — from classical machine learning techniques such as regression, classification, and clustering, to computer vision capabilities like image classification and optical character recognition, to natural language processing services including sentiment analysis and speech recognition, and through to generative AI powered by large language models via Azure OpenAI Service and Azure AI Foundry. The certification was most recently updated on May 2, 2025, with an increased emphasis on generative AI (now weighted at 20–25%), reflecting industry demand. Note: Azure AI Foundry is in the process of being rebranded to Microsoft Foundry, and associated exam materials are being updated accordingly.
This is a fundamentals-level, non-expiring certification priced at approximately $99 USD (varies by region). It is delivered through Pearson VUE for general candidates, or through Certiport for students and educators. The certification serves as a stepping stone toward more advanced role-based credentials such as Azure AI Engineer Associate (AI-102) and Azure Data Scientist Associate (DP-100), though it is not a formal prerequisite for either.
AI-900 is explicitly designed for candidates from both technical and non-technical backgrounds, requiring no prior data science or software engineering experience. It is well-suited for IT professionals, developers, and cloud practitioners who want to establish a formal AI foundation, as well as business stakeholders — project managers, sales professionals, marketing specialists, and executives — who need to understand AI capabilities in order to collaborate with technical teams or identify AI opportunities within their organizations.
Job roles that commonly pursue this certification include aspiring AI Engineers, Data Analysts, Cloud Support Associates, Solutions Architects, and students entering technology fields. It is also valuable for professionals currently working in Azure environments who want to formalize their understanding of Azure's AI and ML service portfolio before pursuing the AI-102 or DP-100 credentials.
There are no mandatory prerequisites for AI-900. Microsoft does not require any prior certification or formal coursework before scheduling the exam. However, candidates benefit from a basic awareness of cloud computing concepts (ideally familiarity with Azure fundamentals) and a general understanding of how client-server applications function. These are not hard requirements, but they help contextualize the Azure-specific content on the exam.
For practical preparation, Microsoft recommends completing the official AI-900 self-paced learning path on Microsoft Learn, which is free and structured around the five exam domains. Candidates with no prior cloud exposure may wish to complete the AZ-900 (Azure Fundamentals) content first, though this is optional. Most candidates with basic technology literacy can prepare adequately in one to two weeks using the official materials.
The AI-900 exam consists of a variable number of questions (Microsoft does not publish a fixed count; typical sittings include approximately 40–60 questions) and must be completed within a 45-minute time limit. The exam may include multiple question types such as multiple choice, drag-and-drop, matching, and interactive scenario-based items. Candidates should expect some questions on Preview Azure features that are in common use, in addition to Generally Available (GA) features.
The exam is delivered online through Pearson VUE (with remote proctoring available) or at a Pearson VUE testing center. Students and educators may schedule through Certiport. A score of 700 or higher on a scale of 100–1000 is required to pass. If a candidate fails, a retake can be scheduled 24 hours after the first attempt; subsequent retake waiting periods vary per Microsoft's exam retake policy. The AI-900 is a fundamentals-level certification and does not expire, so no annual renewal is required.
AI-900 functions primarily as a credential signaling foundational AI literacy, making it valuable both as a standalone certification for non-technical professionals and as a launch point for deeper technical paths. For business-oriented roles — product managers, consultants, sales engineers, and executives — it provides the vocabulary and conceptual framework to evaluate AI solutions, communicate with technical teams, and contribute to AI strategy. For technical candidates, it establishes a documented baseline before pursuing the Azure AI Engineer Associate (AI-102), which commands salaries in the $120,000–$175,000+ range for experienced practitioners.
Job roles commonly associated with AI certifications on the Azure track include AI Engineer, Machine Learning Engineer, Data Scientist, Cloud Solutions Architect, and Data Analyst. Salary ranges for Azure AI-related roles broadly span $96,900 to over $200,000 depending on seniority, specialization, and geography. While AI-900 alone does not qualify candidates for senior technical positions, it is a recognized signal of initiative and foundational knowledge in a hiring market where 81% of hiring managers, according to recent industry surveys, report prioritizing demonstrated AI skills during candidate screening. The certification does not expire, so it retains its value without requiring periodic renewal exams.
5 sample questions with answers and explanations. The full bank has 464 questions, enough for 9 full-length practice exams.
Preview — answers shown1. An accessibility application, 'ReadAloud', aims to help users with visual impairments by reading out digital text content from websites or documents. The AI technology that converts written text into audible, synthesized speech is called:
Explanation
Text-to-Speech (TTS) synthesis is the artificial production of human speech. It converts language text into audible speech, which is exactly what the ReadAloud application needs. STT converts speech to text. OCR extracts text from images. Machine Translation converts text from one language to another.
2. A company is using Azure Machine Learning to train a model that predicts customer lifetime value (a numerical amount). What core machine learning concept describes the 'historical purchase amounts,' 'frequency of visits,' and 'demographic data' used as inputs to the model?
Explanation
Features are the input variables used by a machine learning model to make predictions. In this case, 'historical purchase amounts,' 'frequency of visits,' and 'demographic data' are all features that describe the customer and are used to predict their lifetime value (the label). Labels are the output. Validation datasets test the model. Endpoints serve predictions.
3. A financial services firm, 'SecureInvest', uses an AI model to detect fraudulent transactions. After deploying the model, they need a way to monitor its performance over time and potentially retrain it if its accuracy degrades. Which phase of the machine learning lifecycle does this continuous monitoring and potential retraining primarily belong to?
Explanation
Model monitoring and MLOps practices cover the post-deployment phase of a machine learning model. This includes tracking its performance in a live environment, detecting concept drift or data drift (which can degrade accuracy), and having processes in place for retraining and redeploying the model as needed to maintain its effectiveness. Data collection and training are earlier phases. Deployment is the initial release, while monitoring is ongoing.
4. SmartCity Corp collected data on traffic patterns, weather conditions, and city events to analyze transportation trends. They want to ensure their AI model can make accurate predictions on new, unseen data rather than just memorizing the training examples. What is this ability called in machine learning?
Explanation
Generalization is the correct answer because it refers to a model's ability to perform well on new, unseen data rather than just memorizing the training data. A model that generalizes well has learned the underlying patterns and can make accurate predictions on data it hasn't encountered during training. This is the ultimate goal of machine learning - to build models that work well in real-world scenarios with new data.
5. EcoPredict wants to forecast the monthly energy consumption of office buildings based on factors like size, location, occupancy, and historical usage patterns. What type of machine learning technique is most suitable for this scenario?
Explanation
Regression is the correct technique because the goal is to predict a continuous numerical value (monthly energy consumption) based on input features. Regression models are specifically designed to predict numeric outcomes. Classification would be used to predict categories, clustering to group similar buildings, and reinforcement learning for decision-making scenarios with rewards and penalties.
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