Artificial Intelligence (AI) is a rapidly growing field, and Microsoft Azure has emerged as a prominent player in the AI industry. The Microsoft Azure AI Fundamentals (AI-900) Exam is designed to test your knowledge of the fundamental concepts and principles of AI, as well as your ability to use Azure AI services. This exam is an excellent opportunity for individuals looking to begin their careers in the AI field or enhance their existing skills. Microsoft provides candidates with access to its learning path, which is designed to prepare them for certification tests. This learning path guides candidates through the concepts in a sequential manner. The pathways, on the other hand, include modules that help candidates enhance their skills and knowledge in the following areas:
- Firstly, getting started with artificial intelligence on Azure.
- Secondly, building no-code predictive models with Azure Machine Learning.
- Also, exploring computer vision in Microsoft Azure.
- Lastly, explore natural language processing and conversational AI.
However, many people wonder how hard the AI-900 exam is and what they can expect from the exam. In this blog, we will explore the difficulty level of the Microsoft Azure AI Fundamentals Exam and provide tips to help you prepare for and pass the exam with flying colors.
Microsoft Azure AI Fundamentals Glossary
Here are some key terms and definitions related to Microsoft Azure AI Fundamentals:
- Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems.
- Machine Learning (ML): A subset of AI that uses algorithms and statistical models to enable machines to improve their performance on a specific task over time.
- Deep Learning: A type of machine learning that uses neural networks with many layers to learn complex patterns and features from data.
- Natural Language Processing (NLP): A branch of AI that deals with the interaction between humans and computers using natural language.
- Cognitive Services: Pre-built AI models that allow developers to add intelligent features to their applications, such as speech recognition, language understanding, and image analysis.
- Chatbots: An AI application that uses NLP to simulate human conversation and respond to customer queries.
- Computer Vision: An AI technology that enables machines to interpret and understand the visual world, such as recognizing objects in images and videos.
- Decision Trees: A machine learning algorithm that uses a tree-like model of decisions and their possible consequences.
- Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with its environment and receiving rewards or penalties for its actions.
- Neural Networks: A type of machine learning algorithm inspired by the structure and function of the human brain, which is composed of interconnected neurons.
Exam preparation resources for Microsoft Azure AI Fundamentals (AI-900) exam
Here are some resources that can help you prepare for the Microsoft Azure AI Fundamentals (AI-900) exam:
- Microsoft Learn: Microsoft’s official learning platform offers a wide range of 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.
- Exam AI-900: Microsoft Azure AI Fundamentals Certification Study Guide: This book by Saurabh Pant is a comprehensive guide to help you prepare for the AI-900 exam. It covers all the topics in detail and includes practice questions and answers.
- AI-900 Exam Ref: Microsoft Azure AI Fundamentals: This book by Jim Cheshire provides a concise overview of the exam topics and includes sample questions and exercises.
- Microsoft Azure AI Fundamentals Certification Exam Guide: This free guide by Cloud Academy provides an overview of the AI-900 exam, its objectives, and tips on how to prepare for it.
- Practice tests: Taking practice tests can help you assess your knowledge and identify your weaknesses. Microsoft offers official practice tests for the AI-900 exam, which are available for purchase. https://www.microsoft.com/en-us/learning/exam-AI-900.aspx#practice-exams
Links:
- Microsoft Learn: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
- Exam AI-900: Microsoft Azure AI Fundamentals Certification Study Guide: https://www.amazon.com/Exam-AI-900-Microsoft-Fundamentals-Certification-ebook/dp/B08LDSFGLP
- AI-900 Exam Ref: Microsoft Azure AI Fundamentals: https://www.amazon.com/Exam-Ref-AI-900-Microsoft-Fundamentals/dp/0137533966
- Microsoft Azure AI Fundamentals Certification Exam Guide: https://cloudacademy.com/guide/microsoft-azure-ai-fundamentals-certification-exam-ai-900/
AI-900 Course Outline
Now, the candidate should get an idea about the course structure. Below, we are mentioning the course outline that the candidate should know in order to pass the Al-900 exam.
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
What makes the Microsoft AI-900 Exam Difficult?
Every firm now demands competent applicants who can operate equipment effectively and manage operations efficiently while reducing time waste. In the AI-900 Exam, the candidate will take on the role of Microsoft Certified: Azure AI Fundamentals, putting their understanding of popular machine learning and artificial intelligence workloads to work on Azure. This includes describing considerations for inclusion, openness, and responsibility in an AI solution, as well as identifying elements of anomaly detection and computer vision workloads. The Exam AI-900 becomes a little more challenging as a result of all of this.
Some questions are really tricky, so make sure you understand the difference between the terms and choose the best solution in the real environment. Moreover, there is no straightforward rule to ace the exam. Therefore, the candidate needs to have access to the right resources to enrich their learning and broaden their knowledge horizon. Refer to the following learning resources!
Microsoft AI-900 Exam Study Guide
1. Microsoft Learning Platform
Microsoft offers a variety of learning paths; candidates should go to Microsoft’s official website for more information. On the official website, the candidate will discover all of the necessary information. There are numerous learning courses and documentation available for this exam. It’s not difficult to find relevant content on the Microsoft website. You may also find the study guides here.
2. Microsoft Documentation
When it comes to studying for examinations, Microsoft Documentations is a valuable resource. The candidate will be able to obtain documentation on any topic related to the exam.
3. Instructor-Led Training
Micorosft’s own training programs are available on the company’s website. Instructor-led training is a valuable resource for preparing for exams such as Microsoft Azure AI Fundamentals (AI-900).
4. Testprep Online Tutorials
Microsoft Azure AI Fundamentals (AI-900) on Azure Online Tutorial enhances your knowledge and provides a depth understanding of the exam concepts. Additionally, they also cover exam details and policies. Therefore learning with Online Tutorials will result in strengthening your preparation.
5. Try Practice Test
Practice tests are the only way for a candidate to know how well they’ve prepared. The practice test will assist candidates in identifying their weak areas so that they can focus on improving them. Nowadays, the candidate can choose from a variety of practice examinations available on the internet. We also provide practice exams at Testprep Training, which are quite useful for those who are prepared.
We at Testprep Training hope that this article helped you to get an understanding of how difficult this exam can be! For better preparation, the candidate should practice upper mention learning resources and try practice tests as well. We wish you good luck with your exam!
