Azure AI Fundamentals (AI-900)
- 234 exam-style questions
- Detailed explanations and references
- Simulation and custom modes
- Custom exam settings to drill down into specific topics
- 180-day access period
- Pass or money back guarantee
What is in the package
The content, tone, and tenor of the questions mimic the actual AI-900 exam. Along with the detailed explanations and the exam-taker tips, we have extensively referenced AWS documentation to get you up to speed on all domain areas tested for the AI-900 exam.
Please consider this course the final pit stop so you can cross the winning line with absolute confidence and get Microsoft Certified! Trust our process; you are in good hands.
Complete AI-900 domains coverage
At CertVista, our AI-900 practice exams are meticulously structured to provide complete coverage of every official exam domain. We ensure you are prepared by testing your knowledge of key concepts and your ability to apply them to the practical, scenario-based questions you will encounter on the test.
Describing Artificial Intelligence Workloads and Considerations
Our practice questions ensure you can confidently identify the right AI workload—such as computer vision, NLP, or generative AI—for any given business problem. We move beyond simple definitions to test your ability to apply the crucial principles of responsible AI, presenting realistic scenarios that challenge you to think through fairness, transparency, and accountability. This approach prepares you for the nuanced, application-focused questions that are a hallmark of this domain.
Describing Fundamental Principles of Machine Learning on Azure
We solidify your understanding of core machine learning principles by having you distinguish between techniques like regression, classification, and clustering in practical contexts. Our questions familiarize you with Azure Machine Learning's specific tools, such as its automated ML and designer features. To ensure you are current with the latest industry trends reflected in the exam, we also cover the fundamentals of deep learning and the Transformer architecture that powers modern AI.
Describing Features of Computer Vision Workloads on Azure
This section of our question bank focuses on how AI interprets the visual world. You will face scenario-based questions that require you to select the appropriate Azure AI Vision service for tasks like optical character recognition (OCR), object detection, and image analysis. We also cover the capabilities and ethical considerations of facial detection and analysis, ensuring you understand this sensitive and important application of computer vision.
Describing Features of Natural Language Processing (NLP) Workloads on Azure
Our materials cover the full spectrum of how AI processes human language. You’ll be tested on your ability to map business needs to the correct Azure AI Language service, whether for key phrase extraction, sentiment analysis, or entity recognition. We also provide extensive coverage of Azure AI Speech services, with questions on use cases like real-time transcription, speech synthesis, and language translation, giving you a comprehensive understanding of Azure's NLP offerings.
Describing Features of Generative AI Workloads on Azure
As the most heavily weighted domain, our coverage of generative AI is especially deep. Our practice questions test your knowledge of the Azure OpenAI Service, requiring you to choose the correct model (like GPT-4 or DALL-E) for a specific task. Furthermore, we test you on the fundamentals of prompt engineering and the capabilities of the newer Azure AI Foundry, ensuring you are fully prepared for the most modern and impactful topics on the exam.
We believe that eliminating surprises is the best way to reduce test-day anxiety. Our practice exam engine is designed to meticulously simulate the live Microsoft AI-900 exam environment. This includes a user interface that mirrors the look and feel of the actual test, a countdown timer to help you master time management, and the same question navigation you will use on the day. This allows you to get comfortable with the pressure of a timed exam so you can focus solely on the questions.
A common mistake we see is candidates preparing only for standard multiple-choice questions, leaving them unprepared for the variety of formats on the actual exam. The AI-900 includes different question types to test knowledge in different ways. Our question bank includes every format you might encounter, including:
- Multiple-Choice: Select the single best answer.
- Drag-and-Drop: Match concepts to their definitions or place steps in the correct order.
- Case Studies: Read a short scenario describing a business problem and answer several questions related to it.
- Hot Area: Identify the correct element within a graphic or diagram.
Practicing with all these formats ensures that no question type will catch you off guard.
True learning happens when you understand why an answer is correct, not just that it is. For every single question in our database, we provide a detailed, expert-written explanation that breaks down the underlying concepts. We don't just explain the right answer; we also explain why the other options (distractors) are incorrect. This deepens your understanding of the material and helps you learn to spot and avoid common traps.
To optimize your study time, you need to know exactly where to focus. Our platform includes a personalised progress tracking dashboard that gives you actionable insights into your performance. You can monitor your scores over time and, most importantly, see a detailed breakdown of your performance across each official AI-900 exam domain (e.g., "Generative AI," "Computer Vision"). This allows you to quickly identify your strengths and weaknesses, so you can dedicate your valuable study time to the areas that need the most improvement.
What's in the AI-900 exam
The AI-900 exam is a foundational-level certification that validates your understanding of core AI and ML concepts and your familiarity with the Microsoft Azure services used to build AI solutions. It's designed for a broad audience, including those in technical and non-technical roles, and does not require data science or software engineering experience. From our perspective as trainers, it serves as an excellent entry point for anyone looking to understand the practical applications of AI in a business context.
Who is the target audience for the AI-900 exam?
This exam is intended for anyone who wants to demonstrate a fundamental knowledge of artificial intelligence and its implementation on Azure. This includes IT professionals, developers, data engineers, business decision-makers, and technology decision-makers. No prior experience with Azure is strictly required, but a basic awareness of cloud computing concepts is beneficial.
What is the format and structure of the AI-900 exam?
The AI-900 exam consists of approximately 40-60 questions to be answered within a 60-minute timeframe. Question formats can include multiple-choice, drag-and-drop, case studies, and short-answer questions. A common mistake we see is candidates not managing their time effectively, so it's crucial to pace yourself.
Exam Details at a Glance:
| Feature | Description |
|---|---|
| Exam Code | AI-900 |
| Number of Questions | 40-60 |
| Exam Duration | 60 minutes |
| Question Types | Multiple-choice, drag-and-drop, case studies |
| Passing Score | 700 out of 1000 |
| Cost | Approximately $99 USD (may vary by region) |
| Prerequisites | None |
| Certification Validity | Does not expire |
What skills and topics are covered in the AI-900 exam?
The AI-900 exam assesses your knowledge across several key domains. As of the latest update on May 2, 2025, the domains and their approximate weightings are as follows:
- Describe Artificial Intelligence workloads and considerations (15–20%): This includes identifying features of various AI workloads like computer vision, natural language processing (NLP), and generative AI. It also covers the crucial guiding principles for responsible AI, such as fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability.
- Describe fundamental principles of machine learning on Azure (15–20%): This section focuses on identifying common machine learning techniques like regression, classification, and clustering. You'll also need to be familiar with deep learning techniques and the Transformer architecture.
- Describe features of computer vision workloads on Azure (15–20%): This involves understanding different computer vision solutions, including image classification, object detection, optical character recognition (OCR), and facial detection.
- Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%): You will be tested on your knowledge of NLP scenarios such as key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition and synthesis, and translation.
- Describe features of generative AI workloads on Azure (20–25%): This domain, which has seen an increased focus, covers generative AI solutions and the capabilities of the Azure OpenAI Service and Azure AI Foundry.
How difficult is the AI-900 exam?
In our experience, the AI-900 is one of Microsoft's more approachable fundamentals exams. It is considered beginner-friendly and does not require deep technical expertise. However, candidates new to AI concepts may find some of the terminology challenging.
To pass, you need a score of 700 out of a possible 1,000. It's important to note that this is not a simple percentage; the score is scaled based on the difficulty of the questions.
How does the AI-900 compare to other foundational AI certifications?
When considering a foundational AI certification, it's helpful to compare Microsoft's offering with those from other major cloud providers and vendor-neutral organizations.
| Certification | Provider | Focus | Target audience |
|---|---|---|---|
| Azure AI Fundamentals (AI-900) | Microsoft | Foundational AI concepts and Azure AI services. | Technical and non-technical roles interested in Azure AI. |
| AWS Certified AI Practitioner (AIF-C01) | Amazon Web Services | Basics of AI and machine learning within the AWS ecosystem. | Broad audience including IT support, project managers, and business analysts. |
| Google Cloud Digital Leader | Google Cloud | Foundational knowledge of cloud concepts and Google Cloud products, with a notable strength in AI and data. | Business professionals and those new to cloud. |
| CompTIA Cloud Essentials+ | CompTIA | Vendor-neutral, broad understanding of cloud computing concepts applicable across platforms. | Beginners seeking a general cloud foundation. |
From our analysis, the AI-900 is an excellent choice for those who anticipate working within the Microsoft ecosystem. While AWS holds a larger market share, Azure's integration with enterprise services makes this certification highly valuable. CompTIA's vendor-neutral approach is beneficial for those who want a broader, non-platform-specific understanding.
What are the best ways to prepare for the AI-900 exam?
A structured study plan is key to passing the AI-900 exam. Here is a step-by-step process we recommend:
- Start with the Official Microsoft Learn Path: Microsoft provides a free, comprehensive set of learning modules that cover all the exam objectives. This should be your primary study resource.
- Gain Hands-On Experience: Although the exam is foundational, practical experience is invaluable. We suggest utilizing the Azure free tier to experiment with services like Azure Machine Learning and Azure AI Vision.
- Use Practice Exams to Assess Your Knowledge: Taking practice tests is a critical step. They help you get accustomed to the question formats and identify your weak areas. A common mistake we see is relying on outdated "brain dumps." Instead, use reputable practice exams. At CertVista, we offer hundreds of up-to-date, exam-style questions on a highly realistic test engine to ensure you are fully prepared.
- Engage with the community: Online forums and study groups can be a great source of information and support from others who are also preparing for the exam.
Common mistakes to avoid when preparing for AI-900
The most common mistakes include focusing too heavily on memorization instead of understanding concepts, skipping hands-on practice with Azure services, and underestimating scenario-based questions that require practical application knowledge.
Top 5 mistakes we see at CertVista:
Over-Memorizing Service Names
- Problem: Memorizing without understanding use cases
- Solution: Focus on when and why to use each service
Ignoring Responsible AI Principles
- Problem: Assuming it's just 15-20% of the exam
- Solution: These principles appear throughout all domains
Skipping Hands-On Practice
- Problem: Theoretical knowledge without practical context
- Solution: Use Azure free tier to explore services
Rushing Through Practice Exams
- Problem: Not reviewing incorrect answers
- Solution: Spend equal time reviewing explanations
Neglecting Generative AI Topics
- Problem: Focusing only on traditional ML
- Solution: Study Azure OpenAI Service and Copilot scenarios
Why should you consider the AI-900 certification?
Pursuing the AI-900 certification offers several benefits:
- Builds a Strong Foundation: It provides a solid understanding of fundamental AI and ML concepts.
- Enhances Career Prospects: As AI becomes more prevalent, having a recognized certification can make your resume stand out.
- Serves as a Stepping Stone: It can be the first step towards more advanced role-based certifications like the Azure AI Engineer Associate (AI-102).
- No Expiration: As a fundamentals-level certification, the Azure AI Fundamentals credential does not expire.
Demonstrating your knowledge with this certification shows a commitment to staying current in a rapidly evolving technological landscape.
Conclusion
The Microsoft AI-900 exam represents your gateway into the rapidly growing field of Azure AI services. From our experience at CertVista, where we've helped thousands achieve certification success, the key to passing AI-900 lies in understanding concepts rather than memorizing facts.
With proper preparation using official Microsoft Learn resources, hands-on practice, and quality practice exams, most candidates can confidently pass within 2-3 weeks of focused study. The investment of $99 and 15-20 hours of preparation time yields a credential that opens doors to AI-focused roles and demonstrates your commitment to understanding modern AI technologies.
At CertVista, we provide hundreds of exam-style questions on our highly realistic test engine, designed to mirror the actual AI-900 exam experience. Our practice tests help you identify knowledge gaps and build confidence before your certification attempt.
By Sarah Chen, Microsoft Certified Trainer with 12 years of cloud architecture experience and author of "Azure AI Implementation Strategies"
Sample AI-900 questions
Get a taste of the Azure AI Fundamentals exam with our carefully curated sample questions below. These questions mirror the actual AI-900 exam's style, complexity, and subject matter, giving you a realistic preview of what to expect. Each question comes with comprehensive explanations, relevant documentation references, and valuable test-taking strategies from our expert instructors.
While these sample questions provide excellent study material, we encourage you to try our free demo for the complete AI-900 exam preparation experience. The demo features our state-of-the-art test engine that simulates the real exam environment, helping you build confidence and familiarity with the exam format. You'll experience timed testing, question marking, and review capabilities – just like the actual certification exam.
You are developing a solution that uses the Text Analytics service. You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
Language detection
Key phrase extraction
Entity recognition
Sentiment analysis
To identify the main talking points in a collection of documents, you should use key phrase extraction. Key phrase extraction is a natural language processing (NLP) technique that automatically identifies the most important words and phrases in text data, helping to summarize the central topics discussed. The Azure Text Analytics service provides a dedicated API for key phrase extraction, which scans the document and returns a list of key terms and phrases that represent the main talking points.
Language detection is used to determine the language of the text, not its main points. Entity recognition focuses on identifying specific entities such as people, organizations, or locations, not general topics. Sentiment analysis determines the emotional tone (positive, negative, or neutral) of the text, not its subject.
On the exam, look for verbs like "identify topics," "main points," or "summarize content"—these often refer to key phrase extraction. If the question refers to identifying people, places, or things, think of entity recognition instead.
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
Natural language processing
Anomaly detection
Computer vision
Conversational AI
For this scenario, the company requires a system that can automatically identify bottles based on their shape, which involves analyzing images to distinguish between bottles and other objects. This type of task is best addressed with computer vision.
Computer vision enables machines to interpret and process visual data from the world, such as identifying objects in images or videos. In this context, a camera or sensor could be used to capture images of items placed in the recycling machine, and a computer vision model can determine whether the item matches the desired bottle shape, automating the sorting process effectively.
Natural language processing is focused on text and speech understanding, not image analysis. Anomaly detection identifies unusual patterns or data outliers, but it is not used for recognizing physical shapes in images. Conversational AI is used for building chatbots and virtual agents, not for processing visual input.
Focus on the input type described in the scenario: identifying shapes visually signals use of computer vision. Be cautious with distractions such as anomaly detection, which might sound relevant but are not about visual object classification.
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
Set Primary metric to accuracy.
Enable Explain best model.
Set Max concurrent iterations to 0.
Set Validation type to Auto.
To meet the Microsoft transparency principle for responsible AI, you need to ensure that the chosen machine learning model can be explained and its decisions understood. In Azure Machine Learning's automated machine learning UI, enabling the Explain best model feature provides model interpretability features, which help you understand why the best model made specific predictions. This aligns with the transparency principle, which focuses on making AI systems and their decisions as open and understandable as possible.
Setting the primary metric to accuracy, choosing max concurrent iterations, or changing the validation type affects model performance but does not address transparency or interpretability. Only enabling model explanation directly supports this responsible AI principle.
When asked about responsible or ethical AI principles such as transparency, focus on features that help users understand, explain, or audit the model (for example, model explanation, feature importance). Accuracy and training-related settings do not address ethical principles directly.
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?
Fairness
Privacy and security
Transparency
Inclusiveness
In this scenario, you are designing an AI system that assesses loan applications. Ensuring that the factors and logic behind approval decisions are explainable refers to the responsible AI principle of transparency.
Transparency emphasizes that AI systems should provide intelligible explanations for their actions and decisions. This allows users and stakeholders to understand how outcomes—such as loan approvals or denials—are determined, thereby fostering trust and accountability.
Fairness deals with preventing discrimination and ensuring that the AI system treats all individuals equitably.
Privacy and security focus on safeguarding user data and respecting confidentiality. Inclusiveness ensures that AI systems are designed for a broad set of users, including those with disabilities.
Transparency, specifically, is about being able to clearly communicate how and why the AI system made its decisions, which is crucial in scenarios such as loan approvals where individuals may be significantly impacted.
To answer similar questions, focus on key words in the scenario. If the scenario asks about understanding, explaining, or making AI decisions clear to stakeholders, it's pointing to the transparency principle.
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?
Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.
Use the original dataset for training and evaluation.
Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation.
Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation.
When building a clustering model in Azure Machine Learning designer, you should split the original dataset into two subsets: one for training the model and one for testing. After training your clustering model on the training data, you evaluate the model using the separate testing dataset. This approach ensures an unbiased evaluation of the model's performance and helps in assessing how well the model generalizes to new, unseen data. Using the testing dataset for evaluation is a standard best practice in machine learning to avoid overfitting and to get an accurate measurement of model performance.
Using the original dataset for both training and evaluation can lead to overly optimistic results that don't reflect true model accuracy (due to data leakage). Evaluating on the training set doesn't give you information about the model's performance on unseen data. Lastly, clustering is an unsupervised task, so splitting into features and labels isn't applicable, as there are generally no labels in clustering tasks.
For questions about model evaluation, always remember to use a testing/validation dataset that was not seen by the model during training. Look out for hints on appropriate dataset splitting for unbiased evaluation.
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
- Include one or more faces.
- Contain at least one person wearing sunglasses.
What should you use to analyze the images?
the Detect operation in the Face service
the Analyze Image operation in the Computer Vision service
the Describe Image operation in the Computer Vision service
the Verify operation in the Face service
To solve this scenario, you need to analyze photos for the presence of faces and check if any faces have sunglasses. The Computer Vision service offers the Analyze Image operation, which can detect various visual features in images, including objects and facial accessories like sunglasses.
The Detect operation in the Face service identifies faces and returns face attributes, but to specifically determine if sunglasses are being worn, the Analyze Image operation in the Computer Vision service is more appropriate. It provides information about objects, facial accessories, and activities happening in the image.
The Describe Image operation generates a textual description, but it may not always list all relevant visual details or detect individual accessories reliably.
The Verify operation in the Face service is intended for face comparison and verification scenarios (for example, determining if two faces belong to the same person), not for analyzing image content for sunglasses.
Therefore, the Analyze Image operation in the Computer Vision service is the best choice to meet both requirements: detecting faces and identifying sunglasses.
Pay careful attention to the scenario's requirements and match them to the specific capabilities of Azure AI services: Analyze Image is broader and more aligned with detecting multiple content features such as faces and accessories.
Which scenario is an example of a webchat bot?
From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.
Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews.
Accept questions through email, and then route the email messages to the correct person based on the content of the message.
The best example of a webchat bot among the scenarios described is a system that, from a website interface, answers common questions about scheduled events and ticket purchases for a music festival. A webchat bot is designed to interact with users through a chat interface embedded in a web application, providing automated responses and conversational support.
The other options describe the use of different AI capabilities: translating kiosk queries, performing sentiment analysis on reviews, and email content routing. None of these involve a webchat interface or conversational UI designed to answer user questions interactively within a web context.
On the AI-900 exam, differentiate solution scenarios by identifying the interaction mode. Webchat bots always involve a chat interface, usually accessible from a browser, and provide automated conversational responses. If a scenario mentions chat, conversation, and a website interface, it likely relates to webchat bots.
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
Ensure that all visuals have an associated text that can be read by a screen reader.
Ensure that a training dataset is representative of the population.
Provide documentation to help developers debug code.
Enable autoscaling to ensure that a service scales based on demand.
The correct answer is to provide documentation to help developers debug code. This aligns with the Microsoft transparency principle for responsible AI, which emphasizes that AI systems should be understandable, and users should have access to information about how the system operates. Proper documentation, especially that which aids in debugging and understanding how the AI makes decisions, directly supports transparency by making the system's functions, limitations, and behaviors clear to those developing, deploying, or using the solution.
The option to ensure all visuals have text for screen readers relates more to accessibility than transparency. Making sure a dataset is representative addresses the fairness principle, which focuses on minimizing bias. Enabling autoscaling is related to service reliability and scalability but does not impact transparency about how the AI system works.
Transparency is about providing sufficient information about AI systems so users and stakeholders can understand how and why decisions are made.
Focus on the core of the transparency principle: ensuring that users and developers can access explanations and documentation for how the AI system operates, its decision-making process, and its limitations.
