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Prerequisites

This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.

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

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Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

Introducing Updated Microsoft Certified: Azure AI Fundamentals Designation

This certificate is an opportunity for candidates to showcase their mastery of the Machine Learning (ML) and Artificial Intelligence (AI) workloads and their implementation of Azure services. For that reason, AI-900 exam can be taken by all candidates from technical and non-technical fields. While software engineering and data science skills may not be required for such an evaluation, it is important to have general programming knowledge to stand a chance of cracking this test right away. By and large, the updated Microsoft Certified: Azure AI Fundamentals designation can help you advance your career growth by preparing you for advanced Azure role-based certifications including the Microsoft Certified: Azure AI Engineer Associate and the Microsoft Certified: Azure Data Scientist Associate certificates.

Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

• Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.
• Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.
• Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
• Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.

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