These tests are simulations of what the real exam will be like. If you ace these practice tests, you'll be in good shape for the actual exam.
Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.
The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.
Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.
There are no prerequisites for this course, however students with some IT knowledge or experience will find the concepts easier to understand.
The Fundamentals certifications validate foundational knowledge of cloud computing services and how those services are provided with Microsoft Azure.
Each question has detailed explanations at the end of each set that will help you gain a deeper understanding of the MS Azure services.
The explanation provides an overview of the topic, reference links to Azure docs and a rationale on why the option is correct or incorrect
Skills measured on Microsoft Azure AI-900 Exam
Describe Artificial Intelligence workloads and considerations (15-20%)
Identify features of common AI workloads
identify prediction/forecasting workloads
identify features of anomaly detection workloads
identify computer vision workloads
identify natural language processing or knowledge mining workloads
identify conversational AI workloads
Identify guiding principles for responsible AI
describe considerations for fairness in an AI solution
describe considerations for reliability and safety in an AI solution
describe considerations for privacy and security in an AI solution
describe considerations for inclusiveness in an AI solution
describe considerations for transparency in an AI solution
describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (30- 35%)
Identify common machine learning types
identify regression machine learning scenarios
identify classification machine learning scenarios
identify clustering machine learning scenarios
Describe core machine learning concepts
identify features and labels in a dataset for machine learning
describe how training and validation datasets are used in machine learning
describe how machine learning algorithms are used for model training
select and interpret model evaluation metrics for classification and regression
Identify core tasks in creating a machine learning solution
describe common features of data ingestion and preparation
describe feature engineering and selection
describe common features of model training and evaluation
describe common features of model deployment and management
Describe capabilities of no-code machine learning with Azure Machine Learning studio
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution
identify features of image classification solutions
identify features of object detection solutions
identify features of optical character recognition solutions
identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks
identify capabilities of the Computer Vision service
identify capabilities of the Custom Vision service
identify capabilities of the Face service
identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
Identify features of common NLP Workload Scenarios
identify features and uses for key phrase extraction
identify features and uses for entity recognition
identify features and uses for sentiment analysis
identify features and uses for language modeling
identify features and uses for speech recognition and synthesis
identify features and uses for translation
Identify Azure tools and services for NLP workloads
identify capabilities of the Text Analytics service
identify capabilities of the Language Understanding service (LUIS)
identify capabilities of the Speech service
identify capabilities of the Translator Text service
Describe features of conversational AI workloads on Azure (15-20%)
Identify common use cases for conversational AI
Identify Azure services for conversational AI
The exam is available in the following languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
IMPORTANT: Be aware that the exams will always use product names and terms in English so the learner must be familiar with many terms in English regardless of the language the exam.