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 investment firm uses Azure Anomaly Detector service to monitor stock price feeds. The service is designed to identify unexpected spikes or dips in the data that deviate from established patterns. What is a key characteristic of the pre-built Anomaly Detector service?
Explanation
The Azure Anomaly Detector service is designed for unsupervised anomaly detection in time-series data. Users can send their time-series data to the API without needing to label past anomalies. The service automatically identifies the best-fitting model to find deviations from the normal pattern, making it easy to use and effective for unlabeled data streams.
2. An online retailer, 'CustomerSegments Inc.,' has a large database of customer purchase histories but no predefined customer groups. They want to use AI to automatically group customers with similar buying behaviors together to better understand their market. What type of machine learning approach is most suitable when the goal is to discover natural groupings in unlabeled data?
Explanation
Clustering is a type of unsupervised machine learning. It is used when you have unlabeled data and want to group similar data points together based on their inherent characteristics or features. This is exactly what CustomerSegments Inc. wants to do with their customer purchase histories. Supervised learning (A, D) requires labeled data. Regression predicts numeric values.
3. For a newly deployed classification model in Azure ML, which metric provides a single score that balances both Precision and Recall, and is often used when there's an uneven class distribution or when it's important to consider both false positives and false negatives?
Explanation
The F1 Score is the harmonic mean of Precision and Recall. It provides a single metric that balances these two, and is particularly useful when the class distribution is imbalanced or when you need to consider the trade-off between finding all positives (Recall) and ensuring that your positive predictions are correct (Precision). Accuracy can be misleading with imbalanced classes. AUC measures the ROC curve's performance. True Negative Rate is specificity.
4. Margie's Travel agency is building a chatbot using Azure QnA Maker. Besides answering direct questions from their FAQ document about booking flights or hotel policies, they also want the bot to respond naturally to simple greetings like 'Hello' or 'How are you?'. What feature in QnA Maker allows for these kinds of pre-defined conversational exchanges or small talk?
Explanation
QnA Maker includes a 'Chit-chat' feature that allows developers to easily add pre-defined conversational capabilities to their knowledge base. This enables the bot to handle common social interactions, greetings, and small talk with various personalities (e.g., professional, friendly, witty) without needing to manually create QnA pairs for all of them. Active learning suggests new QnA pairs. Multi-turn is for guided conversations. Synonyms improve matching for specific questions.
5. ShopEasy Retail needs an automated way to process customer receipts. They want to extract specific details like the merchant's name, transaction date, itemized list of purchases with quantities and prices, and the total amount paid. Which Azure AI service is specifically designed to extract structured information from such forms like receipts?
Explanation
Azure AI Form Recognizer is specifically built to understand the layout and relationships between text in forms and documents, such as receipts. It uses pre-built or custom models to extract key-value pairs, tables, and other structured data, making it ideal for extracting details like merchant name, transaction dates, and itemized lists from receipts. Azure AI Vision - Image Analysis can perform OCR to extract raw text but doesn't inherently understand the structure or semantic meaning of receipt fields. While it successfully converts images to text, it lacks the intelligence to identify which text represents the merchant name versus the total amount or transaction date. Azure AI Custom Vision is designed for training custom image classification or object detection models, not primarily for detailed form data extraction. This service excels at identifying objects or categorizing images but doesn't provide the structured data extraction capabilities needed for processing receipts. Azure AI Language - Text Summarization would condense the text of the receipt but not extract specific fields in a structured manner. This service focuses on creating shorter versions of longer text content rather than identifying and extracting specific data points like prices, quantities, and merchant information in a structured format.
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