AI-900: Microsoft Azure AI Fundamentals Practice Q & A June 23

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Complete preparation & practice tests for the AI-900 Microsoft Azure AI Fundamentals. Artificial intelligence and machine learning are all set to dictate the future of technology. The focus of Microsoft Azure on machine-learning innovation is one of the prominent reasons for the rising popularity of Azure AI. Therefore, many aspiring candidates are looking for credible approaches for the AI-900 exam preparation that is a viable instrument for candidates to start their careers in Azure AI. The interesting fact about the AI-900 certification is that it is a fundamental-level certification exam. Therefore, candidates from technical as well as ones with non-technical backgrounds can pursue the AI-900 certification exam. In addition, there is no requirement for software engineering or data science experience for the AI-900 certification exam. The AI-900 certification can also help you build the foundation for Azure AI Engineer Associate or Azure Data Scientist Associate certifications. Skills measured • 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%)Topics covered on each functional group in exam 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, 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 (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 the Azure Bot service