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. The compliance team at Woodgrove Mortgage needs to explain individual loan denial decisions to rejected applicants as required by financial regulations. The AI model has already been deployed with a Responsible AI dashboard configured in Microsoft Foundry. A data scientist is reviewing the interpretability options available in the dashboard. Which interpretability explanation type should the data scientist use to explain why the model denied a specific applicant's loan application? (Select one!)
ALocal explanations, which show the features that most influenced a specific individual prediction
BError analysis, which identifies data cohorts where the model has a higher concentration of failures
CCohort explanations, which compare model performance and feature influence across demographic subgroups
DGlobal feature importance, which shows the most influential features across all model predictions in aggregate
Explanation
Local explanations reveal why the model produced a specific prediction for one particular data point — for example, which applicant features most influenced a single loan denial decision. This directly addresses the regulatory requirement to explain individual outcomes to specific applicants. Global feature importance describes which features matter most to the model across all predictions in aggregate, which is useful for overall model understanding but does not explain any individual case. Cohort explanations compare model behavior across groups such as age brackets or income ranges, which is relevant for fairness analysis rather than individual explanation. Error analysis identifies data segments where the model fails at a higher rate, supporting debugging and improvement efforts rather than explaining why the model produced a specific prediction for one person.
2. Proseware Medical is deploying an AI-powered diagnostic support tool across their hospital network. The legal team requires documentation demonstrating that all AI systems have defined ownership, clear governance policies, and formal processes for addressing patient harm caused by model errors. Which AI governance mechanism should the solutions architect recommend to satisfy the accountability requirement when the AI system causes harm? (Select one!)
ADeploy model cards that describe the training data composition and known model limitations for each diagnostic model
BApply differential privacy techniques using SmartNoise to protect patient training data from extraction attacks
CImplement audit trails, defined role assignments, pre-deployment impact assessments, and redress processes supported by human oversight for high-stakes decisions
DConfigure temperature to 0 on the deployed model to ensure fully deterministic and consistent diagnostic outputs
Explanation
Accountability in responsible AI requires organizational structures where people — not systems — are responsible when harm occurs. This includes defined roles and responsibilities across the AI lifecycle, audit trails logging decisions and model versions, pre-deployment and ongoing impact assessments, feedback mechanisms for reporting issues, and redress processes for addressing harms. Human oversight is especially important for high-stakes medical decisions. Model cards are a transparency mechanism that document what a model is and how it was built, but do not create governance structures for harm accountability. Differential privacy with SmartNoise is a privacy and security control designed to prevent training data reconstruction, not to establish organizational accountability. Configuring temperature to 0 improves output consistency but creates no governance structure and does not define who is responsible when the system causes harm.
3. Fabrikam Bank is building a digital onboarding solution for new customers. The identity verification step requires confirming that the person submitting their identity documents is physically present in real time. The solution must be resistant to spoofing attacks in which a malicious actor presents a photograph, a recorded video, or a three-dimensional mask of a legitimate customer. Which Azure AI capability should the development team integrate into the onboarding workflow? (Select one!)
AAzure AI Vision image analysis with object detection
BAzure AI Content Understanding document extraction
CAzure AI Face liveness detection
DAzure AI Language named entity recognition
Explanation
Azure AI Face liveness detection is specifically engineered to verify that a user is physically present during biometric verification and to reject presentation attacks including photographs, video replays, and three-dimensional masks. It is the appropriate capability for anti-spoofing requirements in identity verification and onboarding workflows. Azure AI Vision image analysis with object detection can identify objects and scenes in static images but is not designed for real-time liveness verification or anti-spoofing defense in identity contexts. Azure AI Content Understanding is designed to extract structured information from documents, forms, images, and multimedia content, making it suitable for document processing tasks but not for verifying live user presence. Azure AI Language named entity recognition identifies and categorizes entities such as names, dates, and locations within text and plays no role in biometric liveness verification.
4. Contoso Creative is building a marketing copy generator using a language model in Microsoft Foundry. During testing, the team observes that generated text frequently repeats the same words and phrases multiple times within a single response. The team wants to reduce within-response word repetition without altering the overall temperature setting or restricting vocabulary diversity. Which inference parameter adjustment should they make? (Select one!)
AIncrease the presence penalty value to discourage any token that has appeared at least once in the prompt or output
BDecrease the top-p value to narrow the token probability distribution
CDecrease the max tokens value to shorten responses before repetition can accumulate
DIncrease the frequency penalty value to discourage tokens that have already appeared multiple times in the output
Explanation
Frequency penalty (range 0–2) specifically reduces the likelihood of tokens that have already appeared repeatedly in the generated output, directly targeting within-response word repetition. As the same word appears more times, frequency penalty progressively discourages reusing it, eliminating repetitive phrasing without changing the overall randomness controlled by temperature. Decreasing top-p narrows the probability distribution used for token sampling, which would reduce vocabulary diversity and could actually increase repetition rather than reduce it. Decreasing max tokens shortens the response but does not address the underlying tendency to repeat words within whatever length is generated. Presence penalty reduces the likelihood of any token that has appeared at least once regardless of how many times, which broadly discourages revisiting any topic or word. This is a less targeted approach than frequency penalty for resolving repetitive wording, as it penalizes first reuse equally to hundredth reuse.
5. Litware Inc. is a financial services company preparing to deploy AI systems for high-stakes lending decisions. Their compliance team is conducting due diligence on Microsoft's internal accountability structures for responsible AI. Which two options are Microsoft organizational bodies responsible for governing responsible AI development and deployment? (Select two!)
Multiple correct answersAAETHER Committee
BResponsible AI Impact Assessment
COffice of Responsible AI
DFairlearn open-source toolkit
EAzure AI Content Safety
Explanation
The AETHER Committee is Microsoft's cross-company advisory body for responsible AI that provides guidance across the organization. The Office of Responsible AI sets the company-wide rules and governance framework for AI development and deployment. The Responsible AI Impact Assessment is a pre-deployment checklist process used by engineering teams, not an organizational governance body. Fairlearn is an open-source toolkit used to assess and mitigate fairness issues in models, not a governance entity. Azure AI Content Safety is a service for detecting and filtering harmful content in AI outputs, not an accountability body.
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