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Exam AI-900: Microsoft Azure AI Fundamentals
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’s not a prerequisite for any of them.
Part of the requirements for: Microsoft Certified: Azure AI Fundamentals
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Important facts you need to know about AI-900: Microsoft Azure AI Fundamentals Exam
Preparing for the exam will help you screen out questions that are irrelevant for this certification. Ingestion of the data is the most important role in AI. Ready to use the data as soon as possible. Anomaly detection refers to all situations where something out of the ordinary is happening. Accountability models are used for the accuracy rate. Transparency refers to using the data for this service. Interoperability is used on the platform. Microsoft AI-900 exam dumps in order to get the best scores with the Microsoft Azure AI Fundamentals Exam. Tech terms used in AZ-900:Microsoft Azure AI Fundamentals Exam. Serviceidentify is used in the process of AI. Files are used to store the data. The AI application is the product of the AI. Learning is used for this purpose to provide better accuracy rate.
Concepts of the AI are explained in the Microsoft AI-900 exam. Image classification is used as the labeling. Recommendation engine is used as the indexing. Intelligent chatbots are used as the chatbot. Community engagement is the chatbot. Custom bot is used as the chatbot. Extracts are used for this purpose. Brainpool is the tool used to perform the extraction. Word embedding is used as the vector training. Dimensionality reduction is used for this purpose. Intelligent chatbot are used as the chatbot.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe features of no-code automated ML - Describe core machine learning concepts - Describe Azure Machine Learning capabilities |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Describe generative AI concepts - Identify responsible AI considerations for generative AI |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
