Best Azure AI-900 Study Guide for AI Fundamentals

To pass the AI-900 exam, you need a solid foundation in AI fundamentals. Microsoft's AI-900 study guide is an excellent resource to get you started.

The AI-900 study guide covers the core concepts of AI, including machine learning, natural language processing, and computer vision. You'll learn about the different types of machine learning algorithms and how to choose the right one for your project.

To get the most out of the AI-900 study guide, it's essential to understand the exam format and content. The AI-900 exam consists of 40 multiple-choice questions, and it covers topics such as AI concepts, deployment, and security.

The Microsoft Fundamentals section of the Azure AI-900 study guide is a crucial part of preparing for the exam. The exam is designed for candidates with foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

Candidates should have a basic understanding of AI and ML concepts, as well as some general programming knowledge or experience. The exam is intended for both technical and non-technical backgrounds, but data science and software engineering experience are not required.

Here are the key skills covered in the Microsoft Fundamentals section of the exam:

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. This technology has revolutionized the way we live and work.

Machine Learning (ML) is a subset of AI that uses algorithms and statistical models to enable machines to improve their performance on a specific task over time. Think of it like a student learning from experience and getting better with each passing day.

Deep Learning is a type of machine learning that uses neural networks with many layers to learn complex patterns and features from data. It's like a superpower that helps machines see and understand the world in a way that's similar to humans.

Natural Language Processing (NLP) is a branch of AI that deals with the interaction between humans and computers using natural language. This is what enables us to have conversations with chatbots and virtual assistants.

Here's a list of key AI concepts:

Microsoft offers a variety of learning paths for the AI-900 exam, which can be found on their official website.

Microsoft Learn is a valuable resource for exam preparation, offering free, self-paced courses and tutorials on various Azure AI topics, including cognitive services, machine learning, and natural language processing.

You can start with the Azure AI Fundamentals learning path on Microsoft Learn.

Practice tests are also essential for assessing your knowledge and identifying your weaknesses. Microsoft offers official practice tests for the AI-900 exam, which are available for purchase.

Here are some key resources for exam preparation:

These resources will help you prepare for the AI-900 exam and demonstrate your knowledge of AI concepts, machine learning, and Microsoft Azure services.

To study for the Microsoft Azure AI-900 exam, you can start by visiting Microsoft's official website, where you'll find a variety of learning paths and resources to help you prepare.

Microsoft's own training programs are available on the company's website, including instructor-led training, which can be a valuable resource for preparing for exams like the AI-900.

You can also find study guides on the Microsoft website, such as the "Exam AI-900: Microsoft Azure AI Fundamentals Certification Study Guide" by Saurabh Pant, which covers all the topics in detail and includes practice questions and answers.

Here are some recommended study guides for the AI-900 exam:

These study guides can help you prepare for the exam and identify areas where you need more practice.

To study for the Microsoft Azure AI Fundamentals (AI-900) exam, candidates should start by visiting Microsoft's official website, where they can find all the necessary information and resources.

Microsoft offers a variety of learning paths, including instructor-led training, online tutorials, and practice tests. These resources can be found on the company's website and are a valuable asset for preparing for the exam.

Microsoft Documentation is a valuable resource for studying for the exam, providing documentation on any topic related to the exam.

The AI-900 exam is a multiple-choice examination that lasts 90 minutes and consists of 40-60 questions. The exam fee is $99, and it is available in English, Japanese, Korean, and Simplified Chinese.

To prepare for the exam, candidates can use the following resources:

The Microsoft Azure AI Fundamentals (AI-900) exam is a valuable certification that highlights your comprehension of AI concepts, machine learning, and Microsoft Azure services.

The AI-900 exam is a comprehensive assessment of your knowledge in AI and ML concepts, as well as related Microsoft Azure services. It's a 180-minute exam that consists of 40-60 questions.

You can expect a mix of question types, including multiple-choice, drag-and-drop, and hot area active screen questions. The exam is designed to test your understanding of various AI workloads and considerations.

The exam is divided into four main sections, each covering a specific area of AI and ML. Here's a breakdown of what you can expect:

Remember to review the responsible AI guiding principles, including fairness, dependability, privacy, inclusion, openness, and accountability, which are also covered in the exam.

Before the exam, make sure to run a System Check to ensure everything is working smoothly.

Be prepared for a variety of question types, including fill-in-the-blank, valid or bogus, and multiple-choice questions.

To avoid getting stuck on one question for too long, pace yourself and be cautious not to spend too much time on each inquiry.

If you're unsure of an answer, simply skip the question and come back to it later.

No breaks will be allowed during the test, so make sure you're well-prepared and focused.

Here are some key tips to keep in mind:

The change log for the exam is a crucial part of your study guide, and it's good to know that some areas have remained unchanged. The audience profile, for example, hasn't seen any changes since April 24, 2024.

Some topics have seen minor changes, such as the description of Azure Machine Learning capabilities and the identification of Azure tools and services for computer vision tasks and NLP workloads. These changes are worth noting, but they're not a major overhaul of the material.

Here's a breakdown of the changes:

It's also worth noting that some new topics have been added, including describing capabilities of Automated machine learning, data and compute services for data science and machine learning, and model management and deployment capabilities in Azure Machine Learning.

Azure Machine Learning offers a range of capabilities to support machine learning workflows. Automated machine learning is available, which can automate the process of building and tuning machine learning models.

Data and compute services for data science and machine learning are also available, providing scalable and on-demand access to computing resources. Model management and deployment capabilities allow for easy management and deployment of machine learning models.

Azure Machine Learning Studio provides a no-code machine learning experience, with features like Automated ML UI and Azure Machine Learning designer. These tools enable users to build and deploy machine learning models without requiring extensive technical expertise.

Here are some key capabilities of Azure Machine Learning:

Azure OpenAI Service also offers natural language generation, code generation, and image generation capabilities, which can be used to build a wide range of applications.

Computer vision solutions are a crucial part of Azure AI, enabling us to analyze and understand visual data from images and videos.

There are four main types of computer vision solutions: image classification, object detection, optical character recognition, and facial detection and facial analysis.

These solutions can be used for a wide range of tasks, from identifying objects in images to detecting faces in videos.

Let's take a closer look at some of the key features of these solutions:

Azure offers several tools and services for computer vision tasks, including the Azure AI Vision service, the Azure AI Face detection service, and the Azure AI Video Indexer service.

These services can be used to build custom computer vision solutions, and they offer a range of features and capabilities, including:

Here's a summary of the key features of these services: