Practice Exams Microsoft Azure AI-900 Azure AI Fundament

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them. The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time. 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. NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material. Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone. This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of: - Basic cloud concepts - Client-server applications You can use Azure AI Fundamentals 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. Skills at a glance - Describe Artificial Intelligence workloads and considerations (15–20%) - Describe fundamental principles of machine learning on Azure (20–25%) - Describe features of computer vision workloads on Azure (15–20%) - Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%) - Describe features of generative AI workloads on Azure (15–20%) - Describe Artificial Intelligence workloads and considerations (15–20%) Identify features of common AI workloads - Identify features of content moderation and personalization workloads - Identify computer vision workloads - Identify natural language processing workloads - Identify knowledge mining workloads - Identify document intelligence workloads - Identify features of generative 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 (20–25%) Identify common machine learning techniques - Identify regression machine learning scenarios - Identify classification machine learning scenarios - Identify clustering machine learning scenarios - Identify features of deep learning techniques 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 Azure Machine Learning capabilities - Describe capabilities of Automated machine learning - Describe data and compute services for data science and machine learning - Describe model management and deployment capabilities in Azure Machine Learning 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 - 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 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 - Describe capabilities of the Azure AI Language service - Describe capabilities of the Azure AI Speech service - Describe capabilities of the Azure AI Translator service Describe features of generative AI workloads on Azure (15–20%) 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 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