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The benefit of obtaining the AI-900: Microsoft Azure AI Fundamentals Exam Certification
For people working in technology, having an Azure certification becomes important. The high demand for Azure certified experts, the high salary and benefits. Microsoft Azure AI Fundamentals Exam Certification give you skills and knowledge about a particular area of technology.
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Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.
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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
Microsoft AI-900 Exam Syllabus Topics:
| Topic | Details |
|---|---|
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 |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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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.
