Exam AI-900: Microsoft Azure AI Fundamentals

Microsoft Azure AI Fundamentals (AI-900) exam is designed for candidates having basic and foundational knowledge in the field of machine learning (ML), artificial intelligence (AI) concepts and related Microsoft Azure services. Moreover, this exam is an opportunity for candidates to demonstrate their knowledge of common ML and AI workloads and how to implement them on Azure. It tests your understanding of fundamental AI concepts, as well as your knowledge of Azure AI services and how they can be used to solve business problems.

For this exam Data science and software engineering experience are not required. However, some familiarity with general programming knowledge or experience would provide an advantage. Further, Azure AI Fundamentals can be used for preparing for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate. 

Target Audience

The Microsoft Azure AI-900 exam, also known as the “Microsoft Azure AI Fundamentals” exam, is designed for individuals who want to demonstrate their foundational knowledge of artificial intelligence (AI) and its applications in Microsoft Azure. The target audience for this exam includes:

• Business decision-makers: This includes executives, business analysts, and other professionals who need to understand how AI can impact their organization’s operations, efficiency, and overall business strategy.
• Technical professionals: This includes developers, engineers, data scientists, and other technical professionals who are responsible for implementing AI solutions on Microsoft Azure.
• Students and educators: This includes students who are interested in pursuing a career in AI or related fields and educators who are teaching courses related to AI and Azure.
• Anyone interested in AI: This exam is also suitable for anyone who is interested in AI and wants to gain a foundational understanding of the concepts and tools used in AI applications.

Learning Objectives

Microsoft provides exam objectives to help candidates in understanding and learning about the concepts before preparation. Moreover, these exam concepts are provided with sections and subsections to make you learn about it in depth.  For AI-900, Microsoft the basic concepts include:

• AI workloads and considerations
• Fundamental principles of machine learning on Azure
• Features of computer vision workloads on Azure and Natural Language Processing (NLP) workloads on Azure
• Features of conversational AI workloads on Azure

Quick Learning Path

The Microsoft Azure AI-900 exam, also known as the “Microsoft Azure AI Fundamentals” exam, measures a candidate’s understanding of AI concepts and their applications in Microsoft Azure. Here’s a learning path to help you prepare for the exam:

• Familiarize yourself with the exam objectives: Before you start preparing for the exam, review the exam objectives and understand what topics you need to cover. Microsoft provides a detailed exam description that outlines the skills measured in the exam.
• Learn the basics of AI: It’s important to understand the basic concepts of AI, including machine learning, deep learning, natural language processing, and computer vision. You can start with online courses, videos, or books that cover the fundamentals of AI.
• Understand Microsoft Azure AI services: Microsoft Azure offers several AI services, including Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. Learn how to use these services to create intelligent solutions for your business needs.
• Practice with Azure AI services: You can practice using Azure AI services by creating sample applications, experimenting with the APIs, and exploring documentation. This will give you hands-on experience and help you understand how to use Azure AI services in real-world scenarios.

Exam Format

Microsoft Azure AI Fundamentals (AI-900) exam consists of 40-60 questions. The Microsoft Azure AI-900 questions types that can be there in the exam include scenario-based single answer questions, multiple-choice questions, arrange in the correct sequence type questions, drag & drop questions, mark review, drag, and drop, etc. However, to pass the exam, a candidate has to score a minimum of 700 or more. To apply for the exam, the examination fee is $99 USD including taxes. For the AI-900 exam, candidates will get various language options that include English, Japanese, Chinese (Simplified), Korean, German, French, Spanish.

Scheduling Exam

Microsoft Azure AI-900 certification exam measures the ability to describe AI workloads and considerations with  fundamental principles of machine learning on Azure. Further, candidates must be familiar with features of computer vision workloads on Azure and Natural Language Processing (NLP) workloads on Azure. For scheduling the exam,

For non-students interested in technology

And, for students or instructors

AI-900 Exam Key Terms

Here are some common terms and concepts related to AI that you may encounter in the Microsoft Azure AI-900 exam:

• Artificial Intelligence (AI): The ability of machines to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
• Machine Learning (ML): A type of AI that enables machines to learn from data without being explicitly programmed. ML algorithms can identify patterns and insights in data and make predictions or decisions based on that data.
• Deep Learning: A subset of ML that uses neural networks to enable machines to learn from large amounts of data. Deep learning can be used for tasks such as image and speech recognition, natural language processing, and autonomous driving.
• Cognitive Services: A set of pre-built APIs and tools in Microsoft Azure that enable developers to add intelligent features to their applications. Cognitive Services include APIs for vision, speech, language, and decision-making.
• Natural Language Processing (NLP): The ability of machines to understand and process human language. NLP can be used for tasks such as language translation, sentiment analysis, and chatbot development.
• Computer Vision: The ability of machines to interpret and understand visual information from the world around them. Computer vision can be used for tasks such as object recognition, facial recognition, and image analysis.
• Chatbot: A computer program that uses NLP to simulate human conversation. Chatbots can be used for customer support, personal assistance, and other applications.
• Big Data: Large volumes of data that are too complex or too large to be processed using traditional data processing techniques. Big data can be used for ML and other AI applications.
• Ethics in AI: The ethical considerations and implications of using AI, including issues such as privacy, bias, transparency, and accountability.

Microsoft AI-900 Exam Course Outline

The updated Microsoft AI-900 exam topics include:

Topic 1: Describe Artificial Intelligence workloads and considerations (15-20%)

1.1 Identify features of common AI workloads

• identifying the features of data monitoring and anomaly detection workloads Anomaly Detector)
• Identify features of content moderation and personalization workloads
• identify computer vision workloads (Microsoft Documentation: Applying content tags to images, Detect common objects in images, Detect popular brands in images)
• identifying natural language processing workloads (Microsoft Documentation: Choosing a natural language processing technology in Azure)
• identify knowledge mining workloads (Microsoft Documentation: Explore knowledge mining)
• Identify document intelligence workloads
• Identify features of generative AI workloads

1.2 Identify guiding principles for responsible AI

• describing the considerations for fairness in an AI solution (Microsoft Documentation: Model performance and fairness (preview))
• explaining the considerations for reliability and safety in an AI solution (Microsoft Documentation: Responsible and trusted AI)
• describing the considerations for privacy and security in an AI solution (Microsoft Documentation: Responsible AI)
• explaining the considerations for inclusiveness in an AI solution (Microsoft Documentation: Responsible and trusted AI)
• describing considerations for transparency in an AI solution (Microsoft Documentation: Identify guiding principles for responsible AI)
• describing considerations for accountability in an AI solution (Microsoft Documentation: Responsible and trusted AI, Identify guiding principles for responsible AI)

Topic 2: Describe fundamental principles of machine learning on Azure (20-25%)

2.1 Identify common machine learning techniques

• identifying regression machine learning scenarios (Microsoft Documentation: Linear Regression)
• identifying classification machine learning scenarios (Microsoft Documentation: Classification modules)
• identify clustering machine learning scenarios (Microsoft Documentation: Clustering modules)
• Identify features of deep learning techniques

2.2 Describe core machine learning concepts

• identifying features and labels in a dataset for machine learning (Microsoft Documentation: Create and explore Azure Machine Learning dataset with labels)
• explaining how training and validation datasets are used in machine learning (Microsoft Documentation: Configure training, validation, cross-validation and test data in automated machine learning)

2.3 Describe Azure Machine Learning capabilities

• Describe capabilities of Automated machine learning (Microsoft Documentation: Automated machine learning (AutoML))
• Describe data and compute services for data science and machine learning
• Describe model management and deployment capabilities in Azure Machine Learning

Topic 3: Describe features of computer vision workloads on Azure (15-20%)

3.1 Identify common types of computer vision solution:

• identifying features of image classification solutions (Microsoft Documentation: Train image classification models with MNIST data and scikit-learn)
• identify features of object detection solutions (Microsoft Documentation: Detect common objects in images)
• identifying features of optical character recognition solutions (Microsoft Documentation: Optical Character Recognition (OCR))
• identify features of facial detection and facial analysis solutions

3.2 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

Topic 4: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)

4.1 Identify features of common NLP Workload Scenarios

• identifying features and uses for key phrase extraction (Microsoft Documentation: How to extract key phrases using Text Analytics)
• identify the features and uses for entity recognition (Microsoft Documentation: Entity Recognition cognitive skill)
• identifying features and uses for sentiment analysis (Microsoft Documentation: What is sentiment analysis and opinion mining)
• identifying features and uses for language modeling (Microsoft Documentation: language detection in Azure Cognitive Service for Language)
• identify the features and uses for speech recognition and synthesis (Microsoft Documentation: Get started with speech-to-text, Speech service)
• identifying features and uses for translation (Microsoft Documentation: Translator service)

4.2 Identify Azure tools and services for NLP workloads

• identifying capabilities of the Azure AI Language Service (Microsoft Documentation: Azure Cognitive Service for Language)
• identify the capabilities of the Azure AI Speech service (Microsoft Documentation: Speech service)
• identify the capabilities of the Azure AI Translator service

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

5.1 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

5.2 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

Exam Policies

Microsoft Certification exam policies help candidates to get all the exam related entails and information with exam giving procedures. These exam policies are the inclusion of certain rules that need to be followed during the exam time or at testing centers.

For More information Visit: Microsoft Azure AI Fundamentals AI-900 Exam FAQs

Preparation Guide for Microsoft Azure AI-900 Exam

Microsoft Learning Platform

Microsoft provides access to learning platforms with various resources that will help in exam preparation. However, make sure to go through the official website of Microsoft.  For the AI-900 exam preparation, it would be best to first go through the Microsoft official website to get authentic information about the exam. You can easily locate the AI-900 page where you can just go through all the necessary information about the AI-900 exam. Furthermore, Microsoft provided access to the Microsoft Docs learning path as well as the Instructor-led training. These provide advantages to the candidates to understand the concepts more accurately and pass the exam. They are:

Microsoft Docs

Microsoft documentation is the source of knowledge that provides detailed information about the AI-900 exam guide. Moreover, you also get to know the different scales of different Azure AI services. Microsoft Docs consists of modules that will help you gain a lot of knowledge about AI and the different services in a sequence.

Understand the Azure AI services

Spend time learning about the different Azure AI services and how they can be used to solve business problems. It’s important to understand the capabilities and limitations of each service.

Azure AI Gallery

The Azure AI Gallery provides a collection of sample projects, code samples, and tutorials that demonstrate how to use Azure AI services to solve real-world problems.

Instructor-led Training

This Instructor-led training course provides the fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure for creating AI solutions. However, this course is not designed for teaching students to become professional data scientists or software developers. But, for building awareness of common AI workloads and the ability to identify Azure services to support them. This AI-900 training course covers hands-on exercises based on Learn modules. Candidates in this can use the content on Learn as reference materials. This course is designed for anyone interested in learning about the types of solutions artificial intelligence (AI) makes possible. But, there must be some level of familiarity with computer technology and the Internet. S

Online Study Groups

While preparing for the exam, online study groups can provide benefits to candidates. That is to say, joining the study groups will help you to stay connected with other people who are on the same pathway as you. Moreover, you can start discussing your query or the issue you are facing related to the exam in this group. By doing so, you will get the best possible answer to your query.

Practice Tests

Practice tests are important for better preparation as by assessing yourself with these tests you will know about your weak and strong areas. Moreover, you will be able to improve your answering skills that will result in saving a lot of time. And, the best way to start doing Microsoft AI-900 exam practice tests is after completing one full topic as this will work as a revision part for you. So, make sure to find the best practice sources. 

Exam Day Tips and Strategies:

1. Arrive early: Make sure you arrive at the testing center with plenty of time to spare. This will help you avoid feeling rushed or stressed.
2. Read the questions carefully: Take the time to read each question carefully and make sure you understand what is being asked.
3. Manage your time: Be mindful of the time and pace yourself. Don’t spend too much time on any one question, and make sure you have enough time to review your answers.
4. Don’t second-guess yourself: Once you’ve selected an answer, don’t second-guess yourself unless you have a good reason to do so.
5. Stay calm and focused: Try to stay calm and focused throughout the exam. If you feel yourself getting stressed or anxious, take a deep breath and try to relax.

Importance of continuous learning and skill development in AI

Continuous learning and skill development are essential in the field of AI. The field is constantly evolving, and new technologies, techniques, and approaches are emerging all the time. It is essential to keep up with these changes and stay abreast of the latest developments.

Here are some reasons why continuous learning and skill development are important in AI:

• Stay up-to-date with the latest technology: The AI field is rapidly evolving, and new technologies and techniques are emerging all the time. By staying up-to-date with the latest developments, you can ensure that you are using the most effective tools and techniques to solve problems.
• Remain competitive in the job market: As AI becomes increasingly important in various industries, there is a growing demand for skilled AI professionals. By continuously learning and developing your skills, you can remain competitive in the job market and increase your earning potential.
• Improve your problem-solving skills: AI requires a strong foundation in problem-solving skills. By continuously learning and practicing, you can improve your ability to solve complex problems and develop more effective solutions.
• Enhance your creativity: AI requires creativity and innovative thinking. By continuously learning and exploring new ideas, you can enhance your creativity and develop new approaches to solving problems.

In summary, continuous learning and skill development are essential in the field of AI. By staying up-to-date with the latest developments and pursuing further certifications and opportunities, you can remain competitive in the job market, enhance your problem-solving skills, and deepen your knowledge and expertise in the field.

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