This practice test course contains 5 complete timed AI-900 practice tests. That's 250+ unique questions to test how well prepared you are for the real exam.
This practice test course is designed to cover every topic, with a difficulty level like a real exam.
Every question has a detailed answer with the links back to the official Microsoft docs.
Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This certification 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’s not a prerequisite for any of them.
Describe AI workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of conversational AI workloads on Azure
KEY FEATURES OF THESE POPULAR PRACTICE EXAMS
258 PRACTICE QUESTIONS: 5 sets of Practice Exams and 3 case study available on Udemy to assess your exam readiness.
EXAM SIMULATION: All Microsoft azure AI-900 Practice Tests are timed and scored (passing score is 70%) mimicking the real exam environment
DETAILED EXPLANATIONS: Every question includes a detailed explanation that explains why each answer is correct or incorrect
PREMIUM-QUALITY: These practice questions are free from typos and technical errors which makes your learning experience much more pleasant
ALWAYS UP TO DATE: Our question bank is constantly updated based on student feedback from the real exam. New questions are added on a regular basis growing our pool of questions
ACTIVE Q&A FORUM: In this discussion board, students ask questions and share their recent exam experience offering feedback on which topics were covered.
RESPONSIVE SUPPORT: Our team of Azure experts respond to all of your questions, concerns or feedback.
Each question has detailed explanations at the end of each set that will help you gain a deeper understanding of the Azure services.
MOBILE-COMPATIBLE - so you can conveniently review everywhere, anytime with your smartphone!
Plus a 30 DAY MONEY BACK GUARANTEE if you're not satisfied for any reason.
NOTE: The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive. NOTE: In most cases, exams do NOT cover preview features, and some features will only be added to an exam when they are GA (General Availability).
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
automated ML UI
AI-900 azure Machine Learning designer 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 features and uses for webchat bots
identify common characteristics of conversational AI solutions Identify Azure services for conversational AI
identify capabilities of the QnA Maker service
identify capabilities of the Azure Bot service