Practice Exams

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:

Basic cloud concepts
Client-server applications

You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Skills at a glance

Describe Artificial Intelligence workloads and considerations (15–20%)
Describe fundamental principles of machine learning on Azure (20–25%)
Describe features of computer vision workloads on Azure (15–20%)
Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Describe features of generative AI workloads on Azure (15–20%)

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

Identify features of content moderation and personalization workloads
Identify computer vision workloads
Identify natural language processing workloads
Identify knowledge mining workloads
Identify document intelligence workloads
Identify features of generative AI workloads

Identify guiding principles for responsible AI

Describe considerations for fairness in an AI solution
Describe considerations for reliability and safety in an AI solution
Describe considerations for privacy and security in an AI solution
Describe considerations for inclusiveness in an AI solution
Describe considerations for transparency in an AI solution
Describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (20–25%)

Identify common machine learning techniques

Identify regression machine learning scenarios
Identify classification machine learning scenarios
Identify clustering machine learning scenarios
Identify features of deep learning techniques

Describe core machine learning concepts

Identify features and labels in a dataset for machine learning
Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

Describe capabilities of Automated machine learning
Describe data and compute services for data science and machine learning
Describe model management and deployment capabilities in Azure Machine Learning

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution:

Identify features of image classification solutions
Identify features of object detection solutions
Identify features of optical character recognition solutions
Identify features of facial detection and facial analysis solutions

Identify Azure tools and services for computer vision tasks

Describe capabilities of the Azure AI Vision service
Describe capabilities of the Azure AI Face detection service
Describe capabilities of the Azure AI Video Indexer service

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

Identify features and uses for key phrase extraction
Identify features and uses for entity recognition
Identify features and uses for sentiment analysis
Identify features and uses for language modeling
Identify features and uses for speech recognition and synthesis
Identify features and uses for translation

Identify Azure tools and services for NLP workloads

Describe capabilities of the Azure AI Language service
Describe capabilities of the Azure AI Speech service
Describe capabilities of the Azure AI Translator service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

Identify features of generative AI models
Identify common scenarios for generative AI
Identify responsible AI considerations for generative AI

Identify capabilities of Azure OpenAI Service

Describe natural language generation capabilities of Azure OpenAI Service
Describe code generation capabilities of Azure OpenAI Service
Describe image generation capabilities of Azure OpenAI Service

What's inside

Syllabus

This is a Half-Length test compared to tests 3 to 6. This one will give you a warm-up and set your expectations of how the actual exam will be formatted.

If you've done the Half-Length test, you are ready for Full-Length to prepare you for your exam; time to increase your stamina.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers all the material outlined in the official knowledge sections, ensuring comprehensive preparation for individuals with both technical and non-technical backgrounds

Supplements topic study material, but should not be the only resource used to prepare for the official exam, so learners should seek additional resources

Questions are shuffled each time the tests are repeated, encouraging learners to understand the underlying concepts rather than memorizing specific answers

Includes detailed explanations and links to reference materials for each question, which supports a deeper understanding of the problem solutions

Requires awareness of basic cloud concepts and client-server applications, which may exclude learners without this foundational knowledge

Can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, indicating its foundational nature

Reviews summary

Effective ai-900 practice

According to learners, this practice exam set for the Azure AI-900 is a useful supplement to study materials, particularly praised for its relevant questions covering the exam syllabus and detailed explanations that often link to Microsoft documentation. Students appreciate the insight into *why* answers are correct. However, some reviewers note that a few questions may feel slightly outdated or contain incorrect answers, suggesting the content could benefit from occasional updates. Overall, it's seen as a strong tool for identifying knowledge gaps before the official test.

Best used alongside other study.

"Definitely not enough on their own, needed other resources."

"Decent supplementary tool, but don't rely solely on this."

Provides detailed answers and links.

"The explanations alone are worth it."

"The detail in the answers was fantastic."

"Solid practice, explanations are generally clear."

Questions align well with exam topics.

"Questions were very similar in style to the actual exam."

"Best practice tests I've used for AI-900. The explanations alone are worth it."

"Helped me gauge my readiness effectively."

Some questions or answers may be outdated.

"Found multiple incorrect answers. Wasted time trying to figure out if I was wrong or the test was."

"Seriously outdated questions, feels like it hasn't been reviewed in a while."

"A few questions felt a bit out of date regarding specific service names."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament with these activities:

Review Basic Cloud Concepts

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Reinforce your understanding of fundamental cloud concepts to better grasp the context of Azure AI services.

Browse courses on Cloud Computing Basics

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Review introductory materials on cloud computing.
Identify key cloud service models (IaaS, PaaS, SaaS).
Understand the benefits of cloud adoption.

Review Client-Server Applications

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Solidify your knowledge of client-server applications to understand how AI services are accessed and utilized.

Browse courses on Client-Server Architecture

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Review the basic principles of client-server architecture.
Understand the roles of clients and servers.
Explore common client-server communication protocols.

Read 'Azure AI Fundamentals: AI-900 Exam Guide'

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Supplement your learning with a dedicated exam guide to reinforce key concepts and identify areas for improvement.

View Putting Knowledge to Work on Amazon

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Obtain a copy of the 'Azure AI Fundamentals: AI-900 Exam Guide'.
Read each chapter carefully, focusing on key concepts.
Complete the practice questions at the end of each chapter.

Four other activities

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Show all seven activities

Complete Azure AI Service Tutorials

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Gain hands-on experience with Azure AI services by following official Microsoft tutorials.

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Identify tutorials for Azure AI Vision, Language, and OpenAI services.
Follow the tutorials step-by-step, creating and deploying AI solutions.
Experiment with different parameters and configurations.

Read 'Programming Microsoft Azure AI Services'

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Explore a programming guide to understand how to integrate Azure AI services into applications.

View Putting Knowledge to Work on Amazon

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Obtain a copy of 'Programming Microsoft Azure AI Services'.
Review the chapters relevant to your areas of interest.
Experiment with the code examples provided in the book.

Create a Presentation on Responsible AI

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Deepen your understanding of responsible AI principles by creating a presentation that explains the key considerations.

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Research the guiding principles for responsible AI.
Create a presentation outlining fairness, reliability, privacy, and other considerations.
Present your findings to peers or colleagues.

Build a Simple Computer Vision Application

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Apply your knowledge of computer vision by building a simple application that uses Azure AI Vision.

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Choose a computer vision task (e.g., image classification, object detection).
Use Azure AI Vision to implement the chosen task.
Deploy and test your application.

Career center

Learners who complete Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning systems. They require a strong grasp of machine learning techniques, data handling, and model deployment. The content of this course is highly relevant to this career, given that its practice questions cover core machine learning concepts, different techniques like regression, classification, and clustering, and machine learning on Azure. Understanding how training and validation work with datasets, as covered in this course, is especially helpful for anyone developing machine learning models. Ultimately, this course helps build the foundation for creating and managing machine learning pipelines.

See salaries and explore the career path for Machine Learning Engineer

Generative AI Specialist

A Generative AI Specialist works with AI models that create new content, such as images, text, and code, requiring a strong understanding of generative AI techniques. This course can help anyone working as a Generative AI Specialist since content covers the features of generative AI solutions, common scenarios for generative AI, and how to work with the Azure OpenAI Service. Understanding the responsible AI considerations for generative AI, as included in this course, is extremely valuable. This course can help anyone seeking to master this emerging specialization.

See salaries and explore the career path for Generative AI Specialist

Computer Vision Specialist

A Computer Vision Specialist focuses on developing AI systems that can interpret and understand images and videos. They need a strong understanding of computer vision techniques and tools. This course is helpful to anyone seeking a role as a Computer Vision Specialist since its practice questions cover many important concepts like image classification, object detection, and facial analysis, within the Azure ecosystem. The ability to differentiate between the tools, as described in this course, makes this course particularly relevant. It gives one a strong grasp of how computer vision works on Azure.

See salaries and explore the career path for Computer Vision Specialist

Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that can understand, interpret, and generate human language. They must be familiar with various NLP techniques and tools. This course may help anyone seeking a career in Natural Language Processing since it covers common NLP scenarios like key phrase extraction, sentiment analysis, and language modeling, within the context of Azure. Knowing how Azure tools and services function can be very useful in one's career as an Natural Language Processing Engineer. This course helps build familiarity with the landscape of resources available.

See salaries and explore the career path for Natural Language Processing Engineer

Artificial Intelligence Specialist

An Artificial Intelligence Specialist develops and implements AI solutions, often working with machine learning, natural language processing, and computer vision. This role requires a strong understanding of AI principles and the ability to apply them to real-world problems. The fact that this course provides practice with many of these concepts, such as machine learning techniques, computer vision, natural language processing and generative AI, makes it highly relevant to anyone seeking a position as an Artificial Intelligence Specialist. This course can help one better understand how to manage datasets, use machine learning techniques, and develop models.

See salaries and explore the career path for Artificial Intelligence Specialist

Data Scientist

Data Scientists analyze large datasets to extract insights and build predictive models. They use machine learning techniques, data visualization, and statistical analysis to solve complex problems. This course can give one a better understanding of fundamental machine learning principles, including regression, classification, and clustering, all of which are critical to an aspiring Data Scientist. The course also helps one explore other AI concepts, such as computer vision and natural language processing, which are often used in data analysis. This course provides the practice to help any aspiring Data Scientist better understand the building blocks of models and data.

See salaries and explore the career path for Data Scientist

Cloud Engineer

A Cloud Engineer implements and manages cloud infrastructure and services. They require a broad understanding of cloud platforms and how to deploy and scale applications. This course is useful to any Cloud Engineer seeking more experience with integrating Azure AI into their cloud deployments since it covers many capabilities, including machine learning and generative AI, on the Azure platform. This course exposes prospective Cloud Engineers to the Azure AI tools that may be relevant to their cloud infrastructure. This course will give one a strong sense of the options within the AI space on Azure.

See salaries and explore the career path for Cloud Engineer

Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud computing solutions, often leveraging AI services. They need a solid understanding of cloud platforms and how to integrate AI technologies. This course, focused on Azure AI fundamentals, is useful because its practice questions cover the various AI services within the Azure cloud platform. This knowledge is essential for any Cloud Solutions Architect looking to integrate AI applications into their cloud infrastructure. A Solutions Architect should have experience with automated machine learning which is covered in this course.

See salaries and explore the career path for Cloud Solutions Architect

AI Ethics Officer

An AI Ethics Officer develops and implements ethical guidelines for the use of AI. They must understand the ethical considerations surrounding AI, such as fairness, transparency, and privacy. This course is relevant to any AI Ethics Officer given the course covers the principles of responsible AI and identifies relevant considerations. This course can be helpful in outlining the necessary steps one must take to ensure an ethical AI practice. It is a strong introduction to the ethics and philosophy of AI.

See salaries and explore the career path for AI Ethics Officer

AI Product Manager

An AI Product Manager defines the vision and strategy for AI products, working with engineering and design teams to bring those products to market. They must understand the capabilities and limitations of AI technologies. This course can be particularly useful for anyone aspiring to become an AI Product Manager as it goes over the various AI workloads. Having a familiarity with machine learning, computer vision, natural language processing, and generative AI, which this course provides, can be highly beneficial. This course can help an AI Product Manager understand the fundamental concepts they need to succeed.

See salaries and explore the career path for AI Product Manager

Technical Trainer

A Technical Trainer educates others on how to use technology, including AI. They need a deep understanding of the technology as well as strong teaching skills. This course may help technical trainers, given it can give them a strong foundation in Azure AI that they can then use to teach others. This course can serve as a useful foundation for anyone teaching Azure AI, in particular.

See salaries and explore the career path for Technical Trainer

Research Scientist

A Research Scientist in AI conducts research to advance the field, often working on machine learning, natural language processing, and computer vision. This role often requires an advanced degree. The course may be useful for a Research Scientist given that it covers the core aspects of the field. Being familiar with machine learning, computer vision, natural language processing, and generative AI, as provided through the practice questions in this course, is definitely helpful to any Research Scientist. This course may help a student of the field.

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AI Consultant

An AI Consultant advises clients on how to implement AI solutions to solve business problems. They need knowledge of various AI technologies and how they apply to different industries. This course may help anyone looking to work as an AI Consultant, as its practice questions survey many of the core aspects of the AI landscape. Having an understanding of machine learning, computer vision, natural language processing, and generative AI, as practiced in this course, is very helpful for any consultant in this field. This course can help better understand the many solutions a business might explore.

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Software Engineer

A Software Engineer writes code for various systems, including those that use AI. Depending on the role, they may need to understand how to integrate AI capabilities into software applications. This course may be helpful for Software Engineers considering integrating Azure AI services into their applications since it provides an overview of many Azure AI features. This course can serve as useful exposure to the AI landscape within Azure services. A Software Engineer who knows their way around AI in Azure can better serve the products they develop.

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Business Analyst

A Business Analyst identifies and solves business problems, often using data. This role can benefit from understanding AI to help recommend solutions and improve business processes. This course may be helpful to Business Analysts who are exploring AI as a part of their work since it provides a useful overview of common AI concepts. While this course focuses on Azure, its concepts may generalize to other platforms. This course may help Business Analysts better evaluate the potential of AI solutions.

See salaries and explore the career path for Business Analyst

Microsoft Azure AI-900 | Azure AI Fundament

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