Microsoft · AI-901
Microsoft Azure AI Fundamentals (AI-901) Practice Tests
Validates foundational knowledge of AI concepts and the technical skills to implement AI solutions using Microsoft Azure. Candidates demonstrate understanding of AI workloads, machine learning principles, computer vision, NLP, and generative AI on Azure, with working knowledge of Python and Azure resources.
Exam Details
Practice Questions
600
≈ 12 practice exams
Duration
60 minutes
Passing Score
700/1000
Difficulty
FoundationalLast Updated
Jun 2026
Topics Covered
Identify AI concepts and responsibilitiesImplement AI solutions using Microsoft FoundryFundamental principles of machine learning on AzureComputer vision workloads on AzureNatural Language Processing (NLP) workloads on AzureGenerative AI workloads on Azure
Microsoft Azure AI Fundamentals (AI-901) Practice Exam Preparation
Use this AI-901 practice exam to prepare for Microsoft Azure AI Fundamentals (AI-901) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for Microsoft AI-901, 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 Identify AI concepts and responsibilities, Implement AI solutions using Microsoft Foundry, Fundamental principles of machine learning on Azure, Computer vision workloads on Azure, and Natural Language Processing (NLP) 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.
Exam Domain Breakdown
Identify AI Concepts and Responsibilities43%
Implement AI Solutions Using Microsoft Foundry58%
Exam Overview
The Microsoft Azure AI Fundamentals certification (AI-901) validates foundational knowledge of artificial intelligence concepts and the practical technical skills to implement AI solutions using Microsoft Azure. Unlike its predecessor (AI-900), which focused on conceptual understanding, AI-901 reflects the current state of AI development by emphasizing hands-on implementation through Microsoft Azure AI Foundry — Microsoft's unified platform for building, deploying, and managing AI applications. The exam covers a broad spectrum of AI workloads including generative AI, agentic AI, computer vision, natural language processing, speech, and information extraction.
Updated as of April 15, 2026, AI-901 is the replacement for the retiring AI-900 exam (which retires June 30, 2026). Candidates are assessed across two core domains: identifying AI concepts and responsibilities (40–45%) and implementing AI solutions using Microsoft Foundry (55–60%). The implementation-heavy structure means candidates must be comfortable with Python coding syntax, the Azure AI Foundry SDK, and Azure resource management — a notable shift from the purely conceptual nature of the previous exam.
Who Should Take This Exam
AI-901 is designed for individuals at the beginning of their career in AI solution development who want to demonstrate both conceptual knowledge of AI and the foundational technical skills to work with Azure AI services. It suits a wide range of roles including aspiring AI engineers, developers, data analysts, IT professionals, and even business decision-makers with a technical bent who want to understand what Azure AI can do in practice.
The certification also serves as a stepping stone for those pursuing more advanced Microsoft credentials such as Azure AI Engineer Associate or Azure Data Scientist Associate — though it is not a formal prerequisite for those exams. Candidates from non-engineering backgrounds can pursue it, provided they are willing to acquire basic Python familiarity and hands-on Azure experience before the exam.
Prerequisites
There are no mandatory prerequisites to register for or sit the AI-901 exam. However, Microsoft recommends that candidates have awareness of basic cloud concepts and how client-server applications work. Because AI-901 has a substantial implementation domain (55–60% of the exam), candidates should also have working knowledge of Python coding syntax and programming techniques, as well as familiarity with Azure resources and the Azure portal.
Familiarity with REST APIs, SDKs, and CLIs is explicitly called out in the official study guide as expected background knowledge. Candidates who lack hands-on Azure experience are strongly encouraged to complete Microsoft's self-paced learning paths on Microsoft Learn and the official instructor-led course (AI-901T00-A: Introduction to AI in Azure) before attempting the exam.
Exam Format
The AI-901 exam is a proctored, closed-book assessment delivered online or at a Pearson VUE testing center. Based on the standard Microsoft fundamentals exam format and third-party sources, it consists of approximately 40–60 scored questions covering multiple question types including multiple choice, drag-and-drop, case studies, and scenario-based questions. The time allotment is approximately 45–60 minutes of active exam time (the total appointment window including check-in is longer).
The passing score is 700 out of 1000, consistent with all Microsoft certification exams. Scores are reported on a scaled basis and are available immediately upon completion. The exam is available in English and is being progressively localized into additional languages including Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, and Indonesian. Candidates whose preferred language is not yet available may request an additional 30 minutes. Microsoft strongly recommends registering with a personal Microsoft account (MSA) rather than an organizational account, as records tied to work/school accounts may be lost if you leave that organization.
Skills Measured
- 1.Domain 1 — Identify AI Concepts and Responsibilities (40–45%): Covers the principles of responsible AI (fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability); how generative AI models work; selecting appropriate AI models based on capabilities and deployment configuration parameters; identifying scenarios for common AI workloads including generative AI, agentic AI, text analysis, speech, computer vision, and information extraction; common text analysis techniques such as keyword extraction, entity detection, sentiment analysis, and summarization; features of speech recognition and synthesis; features of computer vision and image-generation models; and techniques to extract information from text, images, audio, and video.
- 2.Domain 2 — Implement AI Solutions Using Microsoft Foundry (55–60%): Covers hands-on implementation across four areas. Generative AI and agents: creating effective system and user prompts, deploying models in the Azure AI Foundry portal, building lightweight chat client apps using the Foundry SDK, and creating and testing single-agent solutions. Text and speech: building applications with text analysis, responding to spoken prompts via deployed multimodal models, and using Azure AI Speech within Foundry Tools. Computer vision and image generation: interpreting visual input with multimodal models, generating images with generative models, and building vision-capable applications. Information extraction: using Azure Content Understanding in Foundry Tools to extract structured data from documents, forms, images, audio, and video.
Study Tips
- Complete the official Microsoft Learn learning paths mapped to AI-901 — navigate to the exam page at learn.microsoft.com/credentials/certifications/exams/ai-901 and follow the 'Two Ways to Prepare' section for the most current self-paced content aligned to the April 2026 objectives.
- Get hands-on with Azure AI Foundry (ai.azure.com) — since 55–60% of the exam tests implementation, spend time actually deploying models, building lightweight chat apps with the Foundry SDK, and testing agent solutions in the Foundry portal rather than only reading documentation.
- Study the official AI-901 Study Guide at learn.microsoft.com/credentials/certifications/resources/study-guides/ai-901 — the bullet-level objectives under each domain tell you exactly what tasks you may be asked to perform or identify, and are the most reliable indicator of exam scope.
- Build hands-on familiarity with Azure Content Understanding in Foundry Tools — this service (covering document, image, audio, and video extraction) is explicitly listed across multiple sub-objectives in the implementation domain and represents a newer capability that many study guides do not yet cover thoroughly.
- Practice writing Python code using the Azure AI Foundry SDK — the study guide specifically notes that candidates should know Python coding syntax and programming techniques and should be familiar with REST APIs, SDKs, and CLIs, indicating code-reading or code-completion questions are likely.
- Use the Microsoft exam sandbox at aka.ms/examdemo to familiarize yourself with the interface and question types before sitting the real exam, reducing time spent on navigation during the actual test.
- Review Microsoft's Responsible AI principles in depth — with 40–45% of the exam dedicated to AI concepts and responsibilities, all six principles (fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability) must be understood in the context of real AI solution design decisions.
Career Benefits
Earning the Microsoft Certified: Azure AI Fundamentals credential (via AI-901) demonstrates to employers a verified baseline of both AI conceptual knowledge and practical Azure implementation ability — a combination increasingly sought as organizations accelerate AI adoption on the Microsoft platform. The certification is relevant for entry-level roles such as AI developer, cloud solutions associate, AI solutions analyst, and technical roles adjacent to data and AI teams. Because AI-901 involves actual implementation tasks using Azure AI Foundry, it carries more practical signal than purely conceptual fundamentals certifications.
As a fundamentals-level credential, AI-901 is positioned as a launchpad rather than a terminal certification. It provides a natural pathway toward higher-value credentials including Azure AI Engineer Associate (AI-102) and Azure Data Scientist Associate (DP-100), which command significantly higher salaries. According to industry surveys, Azure AI Engineer Associates earn average salaries in the range of $120,000–$160,000 USD in the United States, and holding the fundamentals cert demonstrates commitment to that path. The certification is also eligible for ACE college credit, which may provide academic value for candidates pursuing formal education concurrently.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 12 full-length practice exams.
Preview — answers shown1. A compliance manager at Northwind Financial Group is developing an AI governance policy that includes obligations under the General Data Protection Regulation (GDPR) for AI systems that process personal data. During a training session, she asks a consultant to confirm the correct notification timeline and penalty structure that applies if a security breach exposes personal data. Which two statements accurately describe GDPR requirements in this scenario? (Select two!)
Multiple correct answersAOrganizations must notify the relevant supervisory authority within 72 hours of becoming aware of a personal data breach
BFor the most serious violations, GDPR fines are set at the higher of €20 million or 4 percent of annual global turnover
CGDPR requires organizations to notify only affected individuals, not regulators, when a data breach occurs
DFor the most serious violations, GDPR fines are capped at the lower of €10 million or 2 percent of annual global turnover
EOrganizations must notify all affected individuals of every data breach regardless of the risk level it poses to those individuals
Explanation
GDPR Article 33 requires organizations to notify the relevant supervisory authority within 72 hours of becoming aware of a personal data breach, provided the breach is likely to result in a risk to individuals' rights and freedoms. This strict timeline applies regardless of whether the root cause has been fully investigated. For the most serious infringements — including unlawful processing of personal data — GDPR sets fines at the higher of €20 million or 4 percent of the organization's total annual global turnover, whichever is greater. This structure ensures penalties scale meaningfully for large multinational organizations. Notification obligations under GDPR run to both the supervisory authority and, when a breach is likely to result in a high risk to individuals, to the affected individuals themselves — not exclusively to one group. The €10 million or 2 percent threshold applies to a separate, lower tier of violations covering administrative obligations such as failure to maintain records or appoint a data protection officer, not to the most serious breaches. Individual notification is not required for every breach; it applies specifically when the breach is likely to result in a high risk to the rights and freedoms of the natural persons concerned.
2. Proseware Legal Services is building a contract analysis solution using Azure AI Content Understanding. The solution must automatically process vendor contracts to perform two tasks: (1) assign each contract to one of a predefined set of categories — NDA, Service Agreement, or Master Services Agreement — and (2) pull the exact contract termination date as it literally appears in the document text. Which two field extraction methods should be configured for these requirements? (Select two!)
Multiple correct answersAConfigure the Extract method for the contract category field
BConfigure the Classify method for the contract category field
CConfigure the Generate method for the contract category field
DConfigure the Extract method for the contract termination date field
EConfigure the Generate method for the contract termination date field
Explanation
The Classify method is designed for fields that must be assigned to one of a fixed, predefined set of values. It constrains the output to the declared categories — NDA, Service Agreement, Master Services Agreement — ensuring consistent, structured results regardless of how the contract describes itself. The Extract method retrieves a value that already exists verbatim in the source document, making it the correct choice for the termination date, which appears as literal text in the contract and needs no transformation. The Generate method produces new synthesized content or insights that are not directly present as literal values in the document — for example, generating a summary or inferring an obligation — which is inappropriate for either of these fields. Using Extract for the category field would return uncontrolled text from the document rather than mapping it to the approved category list, resulting in inconsistent values that break downstream processing.
3. A developer at Wide World Importers is building a marketing copy generation assistant using Microsoft Foundry. The team has identified two quality issues: the model frequently repeats the same words and phrases within a single response, and the output often mirrors content already present in the user's input prompt, producing redundant text. Which two inference parameters should the developer adjust to most directly address these issues? (Select two!)
Multiple correct answersAIncrease the temperature above 1.5 to maximize creativity
BIncrease the frequency penalty value
CIncrease the presence penalty value
DDecrease the top-p value to narrow token selection
EDecrease the max tokens value to shorten responses
Explanation
Frequency penalty reduces the likelihood of tokens that have already appeared repeatedly in the generated output, directly addressing within-response word and phrase repetition. Presence penalty reduces the likelihood of tokens already present in the prompt being echoed back into the response, directly addressing the issue of output mirroring the user's input. Increasing temperature adds overall randomness but does not specifically target repetition patterns. Decreasing top-p narrows the token selection pool but does not discourage repetitive content. Decreasing max tokens limits response length but does not reduce repetitive content within that length limit.
4. Adatum Education is building a digital reading platform for K-12 students across 15 countries. The student population includes learners with dyslexia, ADHD, and autism, as well as emerging readers and non-native speakers. The product team wants to integrate an Azure AI service that directly addresses reading comprehension barriers and has demonstrated measurable improvement in comprehension outcomes for these learner profiles. Which service should the team integrate? (Select one!)
AAzure AI Immersive Reader
BAzure AI Content Safety configured with age-appropriate content filtering policies
CAzure AI Translator with automatic language detection and neural machine translation
DAzure AI Language service configured with key phrase extraction and abstractive summarization
Explanation
Azure AI Immersive Reader is specifically designed to improve reading comprehension and accessibility for users with dyslexia, ADHD, autism, cerebral palsy, emerging readers, and non-native speakers. It provides features such as text spacing adjustments, syllable highlighting, parts-of-speech coloring, read-aloud with synchronized word highlighting, picture dictionaries, and line focus mode. The service supports over 100 languages, making it appropriate for a multilingual 15-country deployment. Research conducted by RTI International demonstrated a 10% improvement in reading comprehension for students using Immersive Reader, providing evidence-based justification for its selection in educational contexts. Azure AI Language with key phrase extraction and summarization provides text analytics capabilities for developers processing content, but does not address the reading experience interface or the accessibility needs of learners with disabilities. Azure AI Translator performs language-to-language translation but does not assist users who read in their native language but struggle with comprehension challenges such as dyslexia. Azure AI Content Safety filters harmful or inappropriate content and provides no reading comprehension or accessibility functionality.
5. Contoso Healthcare has deployed an AI-powered patient data processing system across its clinics in Germany. A regulatory investigation reveals a GDPR violation related to improper storage of patient biometric data. Contoso Healthcare's annual global revenue is €250 million. The compliance team needs to understand the maximum financial penalty the organization could face. Which statement correctly describes the maximum GDPR fine they could receive? (Select one!)
AA fixed penalty of €10 million, applied uniformly regardless of company size or revenue
BExactly 4% of their annual global turnover, calculated as €10 million
CA fixed penalty of €20 million, which is the absolute maximum under any circumstance
DThe greater of €20 million or 4% of their annual global turnover, whichever is the higher amount
Explanation
GDPR imposes maximum fines using a 'whichever is higher' structure: either €20 million or 4% of the organization's annual global turnover. For Contoso Healthcare with €250 million annual global revenue, 4% equals €10 million. Since €10 million is less than €20 million, the maximum applicable fine would be €20 million. This structure ensures that large multinational companies face proportional consequences while maintaining a meaningful minimum threshold for smaller organizations. A fixed €10 million ignores the proportional component of GDPR fines. A fixed €20 million ignores the possibility that 4% of turnover could exceed €20 million for very large companies. Quoting only the 4% calculation without considering the higher-of structure misrepresents how GDPR penalties are determined and could lead to significant underestimation of regulatory exposure for small and mid-sized organizations.
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