PDF Version DemoDescribe the Basic Principles of ML on Microsoft Azure (30-35%)
As for this area, it includes the following:
- 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.
- 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.
- 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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
How to Prepare For AI-900: Microsoft Azure AI Fundamentals
Preparation Guide for AI-900: Microsoft Azure AI Fundamentals
Introduction
Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. The objective of the certification is to verify that approved partners have the knowledge and skills to deploy, configure, set up, activate, maintain, troubleshoot, and support all necessary Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam taker level of competence in the functions, features, and services of Azure. The Microsoft AI-900 Certification Exam is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017, and NET Framework. Courses are available online or in-person depending on individual needs.
This article will cover the areas of knowledge required for this exam, how many questions are covered in each area, and who is taking this certification exam.
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 3: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision |
| Topic 5: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI |
