Welcome to the ultimate practice exams course designed to give you the winning edge in your journey to becoming Microsoft Azure AI Fundamentals - AI-900 certified!
Are you ready to pass the Microsoft Azure AI Fundamentals (AI-900) certification exam? Find out by testing yourself with this new offering on Udemy. Each of the 6 full practice tests in this set provides an entire exam’s worth of questions, enabling you to confirm your mastery of the topics and providing you with the confidence you’ll need to take your Microsoft Azure AI Fundamentals (AI-900) Certification exam.
The tests in this set are timed, so you’ll know when you’re taking more time than the official test allows, and at the end of the test, you’ll receive a personal breakdown of the questions you answered correctly and incorrectly to improve your knowledge and make you more prepared to pass the actual Microsoft exam.
AI-900 : Microsoft Azure AI Fundamentals Exam details :
Exam Name: Microsoft Certified - Azure AI Fundamentals
Exam Code: AI-900
Exam Price: $99 (USD)
Number of Questions: Maximum of 40-60 questions,
Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,
Length of Test: 60 Minutes. The exam is available in English and Japanese languages.
Passing Score: 700 / 1000
Languages : English, Japanese, Korean, and Simplified Chinese
Schedule Exam : Pearson VUE
AI-900 : Microsoft Azure AI Fundamentals Certification Exams skill questions:
Skill Measurement Exam Topics:-
Describe Artificial Intelligence workloads and considerations (20–25%)
Describe fundamental principles of machine learning on Azure (25–30%)
Describe features of computer vision workloads on Azure (15–20%)
Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)
##) Describe Artificial Intelligence workloads and considerations (20–25%)
Identify features of common AI workloads
Identify features of anomaly detection workloads
Identify computer vision workloads
Identify natural language processing workloads
Identify knowledge mining 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 (25–30%)
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 capabilities of visual tools in Azure Machine Learning Studio
Automated machine learning
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 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 (25–30%)
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 Language service
Identify capabilities of the Speech service
Identify capabilities of the Translator service
Identify considerations for conversational AI solutions on Azure
Identify features and uses for bots
Identify capabilities of Power Virtual Agents and the Azure Bot service
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.
You may be eligible for ACE college credit if you pass this certification exam. See ACE college credit for certification exams for details.
Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; and features of Natural Language Processing (NLP) workloads on Azure.