Are you wondering if you’ll be able to pass the Microsoft Azure AI Fundamentals (AI-900) Exam or not? Do you want to know how hard the exam will be? If yes, then keep on reading. We have curated this article to explain every basic detail about the Al-900 exam with a study guide to help you prepare better. To begin with, the Microsoft Azure AI Fundamentals (AI-900) exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. Further, the Microsoft Azure AI Fundamentals (AI-900) exam is designed for applicants who have a basic understanding of machine learning (ML), artificial intelligence (AI), and related Microsoft Azure services.
In addition, this exam is intended for candidates with both technical and non-technical backgrounds. Experience with data science and software engineering is not essential; however, any programming knowledge or experience would be advantageous. The Azure AI Fundamentals can also be used to prepare for other Azure role-based certifications, such as Azure Data Scientist Associate and Azure AI Engineer Associate, but it isn’t required for any of them.
Microsoft A9-100 Exam Details
To your knowledge, 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, exploring natural language processing and conversational AI.
Passing the exam demonstrates professional proficiency in the core subject areas covered by the equivalent Microsoft exam, and it demonstrates that you are ready to start a career designing programs for Microsoft Windows operating systems.
Exam Format
In order to know the difficulty level, one should know the basic details of the exam.
- So, the Microsoft Azure AI Fundamentals (AI-900) exam consists of 40-60 questions.
- Also, the types of questions that candidates can face 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.
- Furthermore, to apply for the exam, the examination fee is $99 USD including taxes.
- In addition, for the AI-900 exam, candidates will get various language options that include English, Japanese, Chinese (Simplified), Korean, German, French, and Spanish.
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.
Microsoft AI-900 Exam has updates in the course outline as on April 29, 2022.
The updated Microsoft AI-900 exam topics include:
Topic 1: Describe Artificial Intelligence workloads and considerations (20-25%)
1.1 Identify features of common AI workloads
- identifying the features of anomaly detection workloads Anomaly Detector)
- 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)
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 (25- 30%)
2.1 Identify common machine learning types
- 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)
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 capabilities of visual tools in Azure Machine Learning studio:
- automated Machine Learning (Microsoft Documentation: Automated machine learning (AutoML))
- Azure Machine Learning designer (Microsoft Documentation: Azure Machine Learning designer)
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, facial recognition, and facial analysis solutions (Microsoft Documentation: Face detection and attributes, Face recognition concepts)
3.2 Identify Azure tools and services for computer vision tasks
- identify the capabilities of the Computer Vision service (Microsoft Documentation: Computer Vision)
- identifying capabilities of the Custom Vision service (Microsoft Documentation: Custom Vision)
- identify the capabilities of the Face service (Microsoft Documentation: Azure Face service)
- identifying capabilities of the Form Recognizer service (Microsoft Documentation: Form Recognizer)
Topic 4: Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)
4.1 Identify features of common NLP Workload Scenarios
- identifying features and uses for keyphrase 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 Language Service (Microsoft Documentation: Azure Cognitive Service for Language)
- identify the capabilities of the Speech service (Microsoft Documentation: Speech service)
- identify the capabilities of the Translator Text service (Microsoft Documentation: Text Translation)
4.3 Identify considerations for conversational AI solutions on Azure
- identifying features and uses for bots (Microsoft Documentation: Web Chat overview)
- identify the capabilities of the Azure Bot Service (Microsoft Documentation: Azure Bot 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!
