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 data science team at Adatum Healthcare has deployed a patient readmission risk model. A hospital administrator wants to understand which factors most influence the model's predictions across all patients in aggregate. A treating physician separately wants to understand why a specific individual patient received a high-risk score. Which two types of explanations from the Azure Machine Learning Responsible AI dashboard should the data science team provide to address these two audiences? (Select two!)
Multiple correct answersAGlobal explanation showing overall feature importance across all model predictions
BLocal explanation showing feature contributions for a specific individual prediction
CCounterfactual what-if analysis modifying training data distributions
DFairness assessment comparing prediction accuracy across demographic groups
EResponsible AI Scorecard summarizing the model's compliance posture
Explanation
Global explanations reveal which features most influence the model's predictions across the entire dataset — helping the hospital administrator understand the model's general decision-making patterns and validate that clinically relevant factors drive predictions overall. Local explanations, using techniques such as SHAP values or LIME, show the specific feature contributions that led to a particular prediction for an individual patient — exactly what the treating physician needs to understand why a specific person received a high-risk score. Counterfactual what-if analysis explores how changing specific input features would alter model outputs, which supports feature exploration but is not the primary explanation mechanism for either audience described. Fairness assessment evaluates model performance disparities across demographic groups, addressing equity concerns rather than prediction explainability. The Responsible AI Scorecard is a customizable PDF report designed for audit and stakeholder communication, not for real-time per-patient explanation delivery.
2. A technology lead at Northridge Ventures is establishing an AI safety and fairness program for their newly deployed machine learning models. They need tools that will (1) simulate adversarial cybersecurity attacks against their AI models to identify vulnerabilities before production release, and (2) evaluate whether the models perform equitably across different demographic subgroups. Which two Microsoft tools should they select for these specific purposes? (Select two!)
Multiple correct answersACounterfit
BAzure AI Content Safety
CFairlearn
DSmartNoise
EResponsible AI Scorecard
Explanation
Counterfit is Microsoft's open-source cybersecurity testing tool that simulates adversarial attacks against AI models, helping teams identify vulnerabilities before deployment. Fairlearn is Microsoft's open-source toolkit specifically designed to assess and mitigate fairness issues by analyzing model performance across demographic subgroups using metrics such as demographic parity and equalized odds. Azure AI Content Safety monitors and filters harmful content in model inputs and outputs but does not simulate adversarial attacks or assess demographic fairness distributions. SmartNoise is a differential privacy toolkit co-developed by Microsoft for protecting individual data privacy in aggregate analyses, not for adversarial attack simulation or subgroup fairness evaluation. The Responsible AI Scorecard generates customizable PDF reports for stakeholders and auditors but is a reporting artifact rather than an active testing or evaluation toolkit.
3. A security team at Northwind Insurance is preparing to launch a customer-facing generative AI assistant built on Microsoft Foundry. Before public release, the team conducts internal sessions where specialists craft adversarial inputs specifically designed to manipulate the model into ignoring its system instructions, bypassing its content policies, and producing outputs that violate the organization's usage guidelines. Which responsible AI practice is the team performing, and which AI-specific threat category does this exercise primarily target? (Select one!)
ARunning a Responsible AI Impact Assessment to identify whether the model's training data contains private customer information that could be memorized and reproduced in responses
BConducting red-teaming exercises designed to uncover prompt injection and jailbreaking vulnerabilities that allow adversarial users to bypass safety guardrails
CExecuting error analysis to identify cohorts of users or query types that receive lower-quality or inconsistent model responses compared to the baseline population
DPerforming a fairness assessment using the Fairlearn toolkit to detect whether the model produces biased or inequitable responses for specific demographic groups
Explanation
The practice described is red-teaming, a responsible AI reliability and safety technique where specialists deliberately probe an AI system for vulnerabilities by crafting inputs designed to defeat its safety defenses. The specific threats being tested are prompt injection, where adversarial inputs override or manipulate the model's system instructions, and jailbreaking, where users attempt to circumvent safety guardrails through cleverly structured prompts. Both are classified as generative AI-specific security threats that fall under the Reliability and Safety responsible AI principle. Mitigations include implementing guardrails for input validation, output filtering, and deploying Azure AI Content Safety. A Responsible AI Impact Assessment is a governance checklist completed before deployment, not an adversarial testing exercise. Fairness assessment using Fairlearn focuses on detecting demographic bias and inequitable outcomes, not safety guardrail vulnerabilities. Error analysis identifies cohorts with elevated error rates to improve model quality, but does not involve adversarial probing for exploitable weaknesses.
4. Adatum Publishing is building an educational reading platform for primary school students. Several students have dyslexia, ADHD, and autism spectrum disorder. The accessibility team wants to incorporate an Azure AI service that helps students read and comprehend text content. The solution must support over 100 languages and has demonstrated measurable reading comprehension improvement in independent research studies. Which Azure AI service should they implement? (Select one!)
AAzure AI Speech with neural text-to-speech, configured to read page content aloud in multiple languages
BAzure AI Language with abstractive summarization, configured to simplify complex passages for younger readers
CAzure AI Translator, configured with automatic language detection and real-time text translation
DAzure AI Immersive Reader, which provides reading aids designed for users with dyslexia, ADHD, and autism and has demonstrated a 10% reading comprehension improvement
Explanation
Azure AI Immersive Reader is specifically designed to help users with reading challenges including dyslexia, ADHD, autism, and cerebral palsy, as well as emerging readers and non-native speakers. It supports over 100 languages and an independent RTI International study demonstrated a 10% reading comprehension improvement, making it the appropriate service for this inclusive educational platform. Azure AI Translator provides language translation but is not designed for reading comprehension support or accessibility aids such as syllable highlighting, picture dictionaries, and focus modes. Azure AI Speech text-to-speech can read content aloud but lacks the specialized accessibility features that Immersive Reader provides for users with reading difficulties. Azure AI Language summarization can simplify text but does not address the specific accessibility needs of users with dyslexia, ADHD, or autism, and is not the designated inclusiveness tool within the Azure AI portfolio.
5. Litware Customer Services handles thousands of customer support chat conversations daily. After each conversation ends, the support team wants to automatically generate concise post-conversation summaries capturing the key issue reported, the resolution steps taken, and the final outcome. The summaries must read as fluent, coherent paragraphs written in natural language rather than as verbatim copied fragments extracted from the original transcript. Which Azure AI Language summarization approach should they use? (Select one!)
AOpinion Mining, which identifies specific aspects and the sentiments customers expressed about each topic during the conversation
BExtractive summarization, which selects and returns the most important original sentences verbatim from the conversation transcript
CKey Phrase Extraction, which identifies and returns the most significant terms and topics mentioned across the conversation
DAbstractive summarization, which generates new fluent sentences that synthesize and rephrase the key points from the conversation
Explanation
Abstractive summarization generates entirely new sentences that rephrase and synthesize the key information from the source content, producing fluent, coherent, and readable summaries that integrate information across multiple conversation turns. This approach is ideal when the output must read naturally as a composed paragraph rather than as a collection of copied fragments. Azure AI Language supports abstractive summarization for both documents and conversations. Extractive summarization selects verbatim sentences from the original transcript, meaning the output consists of unchanged original sentences that may lack narrative coherence, miss synthesized conclusions spanning multiple turns, or repeat conversational filler language. Key Phrase Extraction returns a list of the most important terms and topics in the text but does not generate any form of readable prose summary. Opinion Mining identifies specific entities and the sentiments expressed about them within text, which is well suited for product review or feedback analysis but does not produce structured conversation summaries capturing issues, resolutions, and outcomes.
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