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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

Download exam skills outline

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:

• Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
• Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
• Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
• Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
• Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.

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Describe NLP Workloads Features on Azure (15-20%)

This domain contains the following details that you need to learn about:

• Identify the features of basic NLP (Natural Language Processing) Workload Scenarios – The individuals should be able to identify various uses and features of various components, for example, keyphrase extraction, sentiment analysis, entity recognition, translation, language modeling, and speech recognition & synthesis.
• Identify Azure services & tools for Natural Language Processing Workloads – This topic is created to equip you with the ability to identify various capabilities, such as Speech service, Text Analytics service, Translator Text service, and Language Understanding service.

Explain Computer Vision Workloads Features on Azure (15-20%)

Here, the following skills will be measured:

• Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
• Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.

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