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ai-900 microsoft azure ai fundamentals
AI-900 Microsoft Azure AI Fundamentals is an entry-level certification offered by Microsoft that validates the foundational knowledge of individuals in Artificial Intelligence (AI) and its practical applications on Microsoft Azure. This certification is designed for individuals who want to build a strong foundation in AI and Azure and want to pursue a career in AI or Cloud computing.
To obtain the AI-900 certification, candidates must demonstrate an understanding of the following concepts:
- AI workloads and considerations
- Fundamental principles of machine learning
- Computer vision concepts
- Natural language processing concepts
- Conversational AI concepts
- Responsible AI
The certification exam consists of 40-60 multiple-choice questions and must be completed within 60 minutes. The exam can be taken online or in-person at a Microsoft testing center.
By earning the AI-900 certification, individuals can demonstrate their knowledge and skills in AI and Azure, which can help them stand out in the job market and pursue roles such as AI Engineer, Azure AI Engineer, Data Scientist, and many others.
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Azure AI Fundamentals Certification (AI-900) – Full Course to PASS the Exam
What is Azure AI Fundamentals AI 900?
Azure AI Fundamentals AI-900 is an entry-level certification exam offered by Microsoft that validates a candidate’s understanding of Artificial Intelligence (AI) concepts and their ability to work with Microsoft Azure’s AI services. The certification exam is designed for individuals who want to demonstrate their knowledge of AI and its practical applications using Microsoft Azure.
To earn the Azure AI Fundamentals certification, candidates are required to pass the AI-900 certification exam, which covers various topics such as:
- The principles of AI and machine learning
- The Azure AI portfolio, including Cognitive Services, Machine Learning, and Bot Services
- Use cases for AI and common business scenarios where AI can be applied
- Ethical and responsible AI development and deployment practices
- The process of creating an AI solution using Azure AI services
The Azure AI Fundamentals certification is ideal for beginners who are interested in learning about AI and how it can be used to solve real-world problems. It is also useful for professionals working in non-technical roles who want to understand the capabilities of AI and how it can impact their work.
Is Azure AI Fundamentals exam hard?
The difficulty of the Azure AI Fundamentals exam (AI-900) can vary depending on the individual’s level of knowledge and experience with artificial intelligence (AI) concepts and Azure services. However, generally speaking, the exam is considered to be an entry-level exam, meaning it is designed to test foundational knowledge of AI and Azure AI services.
The exam covers topics such as machine learning, cognitive services, natural language processing, computer vision, and Azure AI services. It consists of 40-60 multiple-choice questions and has a time limit of 60 minutes.
If you have a solid understanding of the topics covered in the exam and have hands-on experience with Azure AI services, you may find the exam relatively easy. However, if you are new to AI and Azure, or if you have limited experience with the services covered in the exam, you may find it challenging.
Overall, the best way to prepare for the exam is to study the exam objectives, review the official Microsoft documentation and online training resources, and practice using Azure AI services.
What is Azure AI fundamentals?
Azure AI Fundamentals is a certification exam offered by Microsoft that validates foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts and their application in Microsoft Azure cloud services.
The certification is designed for individuals who want to learn about the benefits and uses of AI, as well as those who want to gain basic knowledge of the various Azure AI services and how they can be utilized to build intelligent solutions.
The exam covers a wide range of topics, including AI workloads and considerations, computer vision and natural language processing, machine learning, and Azure AI services such as Cognitive Services, Azure Machine Learning, and Azure Bot Service.
The certification exam is intended for individuals with non-technical backgrounds who want to understand AI and its potential applications in business, as well as for technical professionals who want to broaden their knowledge and skills in AI and ML on the Azure platform.
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microsoft azure virtual training day: ai fundamentals
Microsoft Azure Virtual Training Day: AI Fundamentals is a free one-day training event designed to introduce participants to the basics of Artificial Intelligence (AI) and the Azure AI services. The event covers a range of topics related to AI, including machine learning, deep learning, cognitive services, and more.
The training is aimed at individuals with a technical background who are interested in learning about AI and how it can be applied to real-world problems. It is suitable for developers, data scientists, and IT professionals who are new to AI or looking to expand their knowledge.
During the training, participants will learn about:
- The fundamentals of AI, including its history, applications, and key concepts.
- The Azure AI services, including Azure Cognitive Services, Azure Machine Learning, and Azure Databricks.
- How to build, train, and deploy machine learning models using Azure Machine Learning.
- How to use Azure Cognitive Services to add intelligent features to applications.
- Best practices for designing and implementing AI solutions on Azure.
The training is delivered through a mix of live presentations, demonstrations, and hands-on labs. Participants will have the opportunity to ask questions and interact with the trainers and other attendees.
At the end of the training, participants will receive a certificate of completion and will have gained the foundational knowledge needed to start building AI solutions on Azure.
Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and natural language processing. AI is based on the idea of building machines that can “learn” and improve their performance through experience.
Here are some of the fundamentals of AI:
- Machine learning: It is a subset of AI that enables machines to learn from data without being explicitly programmed. It involves the use of algorithms that can learn patterns from data and use them to make predictions or decisions.
- Deep learning: It is a subfield of machine learning that involves the use of neural networks, which are artificial systems modeled after the human brain. Deep learning enables machines to learn from large amounts of data and perform tasks like image recognition, speech recognition, and natural language processing.
- Natural language processing (NLP): It is a subfield of AI that involves the use of computers to understand, interpret, and generate human language. NLP is used in applications like chatbots, virtual assistants, and sentiment analysis.
- Computer vision: It is a subfield of AI that involves the use of computers to interpret and analyze visual data from the world. Computer vision is used in applications like self-driving cars, facial recognition, and object detection.
- Reinforcement learning: It is a type of machine learning that involves training an AI agent to make decisions in an environment by rewarding it for good decisions and punishing it for bad decisions.
- Robotics: It is a branch of engineering that involves the design, construction, and operation of robots. AI is often used in robotics to enable robots to perform complex tasks like navigation, manipulation, and decision making.
- Ethics: As AI systems become more sophisticated, there is a growing need to address ethical concerns such as bias, privacy, and the impact of AI on society. Ethical considerations are becoming increasingly important in the development and deployment of AI systems.
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