Are you preparing for the Exam AI-900: Microsoft Azure AI Fundamentals? Look no further! This study guide is designed to provide you with a summary of the topics covered in the exam and valuable resources to aid your preparation. Whether you’re a technical expert or a non-technical individual, this guide will help you navigate the complex world of machine learning (ML) and artificial intelligence (AI) concepts in the context of Microsoft Azure.
Purpose of this Document
This study guide aims to give you a clear understanding of what to expect on the exam. It includes a summary of the topics that the exam might cover, and most importantly, it provides you with valuable links to additional resources. These resources are carefully selected to help you focus your studies and successfully prepare for the exam.
When taking the exam, it’s crucial to be familiar with the measured skills and their corresponding weightage. By reviewing this study guide, you’ll gain a clear picture of the skills that will be assessed and their relative importance in the overall exam. This will enable you to allocate your study time effectively and streamline your preparation.
Updates to the Exam
Microsoft regularly updates its exams to reflect the skills required to perform various roles. It’s important to note that the English language version of the exam is always updated first, with localized versions following approximately eight weeks later. However, there may be instances where localized versions are not updated according to this schedule. If the exam is not available in your preferred language, you can request an additional 30 minutes to complete it.
Exam Structure and Skills Measured
The Exam AI-900: Microsoft Azure AI Fundamentals focuses on assessing your knowledge of machine learning and artificial intelligence concepts, as well as your familiarity with related Microsoft Azure services. The exam is divided into four main skill areas, each with its corresponding weightage:
Describe Artificial Intelligence Workloads and Considerations (20-25%)
This section evaluates your ability to identify and describe the features of common AI workloads. You should be able to recognize anomaly detection, computer vision, natural language processing, and knowledge mining workloads. Additionally, you’ll need to understand the guiding principles for responsible AI, such as fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability.
Describe Fundamental Principles of Machine Learning on Azure (25-30%)
Here, you’ll be tested on your knowledge of common machine learning types, including regression, classification, and clustering scenarios. You should also have a solid grasp of core machine learning concepts, such as features, labels, training and validation datasets. Furthermore, understanding the capabilities of visual tools in Azure Machine Learning Studio, such as automated machine learning and Azure Machine Learning designer, is crucial.
Describe Features of Computer Vision Workloads on Azure (15-20%)
This section assesses your understanding of the features of different computer vision solutions. You should be able to identify image classification, object detection, optical character recognition, and facial detection and analysis solutions. Familiarity with the capabilities of Azure tools and services for computer vision tasks, such as the Computer Vision service, Custom Vision service, Face service, and Form Recognizer service, is a must.
Describe Features of Natural Language Processing (NLP) Workloads on Azure (25-30%)
In this part of the exam, you’ll demonstrate your knowledge of common NLP workload scenarios, including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition and synthesis, and translation. Understanding the capabilities of Azure tools and services for NLP workloads, like the Language service, Speech service, and Translator service, is essential. Additionally, you should be familiar with the considerations for conversational AI solutions on Azure, including the features and uses of bots, Power Virtual Agents, and the Azure Bot service.
To ensure your success in the exam, we recommend that you train and gain hands-on experience. Microsoft offers a variety of self-study options, including learning paths, modules, and instructor-led courses. These resources will help you develop a deep understanding of the exam topics and enhance your practical skills.
Additionally, you can find documentation, community sites, and videos to supplement your learning. Microsoft Q&A and the Microsoft Tech Community are excellent platforms for getting answers to your specific questions. Furthermore, the AI Show and other Microsoft Learn shows provide valuable insights into the world of AI and ML.
Remember, the more you engage with study resources and gain practical experience, the better prepared you’ll be for the exam.
The Exam AI-900: Microsoft Azure AI Fundamentals is an excellent opportunity to demonstrate your knowledge of AI and ML concepts in the context of Microsoft Azure. This study guide provides you with a comprehensive overview of the exam topics and directs you to valuable resources to aid your preparation. By following this guide and dedicating sufficient time to study and hands-on experience, you’ll increase your chances of acing the exam.
For more information and a deeper understanding of the topics covered in the exam, visit Ratingperson. This platform offers a wealth of resources to help you on your journey to becoming an AI expert. Good luck!