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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-900-mock-tests/
课程评论：没有评论
课程名称：AI-900 模拟测试 概述：本课程旨在帮助您为AI-900考试做好准备，涵盖所有最新的变化和问题。通过多次练习这些测试，您将能更有效地进行备考。AI-900考试的主要内容包括： 1. **描述AI工作负载和考虑因素（15-20%）** 2. **描述Azure上机器学习的基本原则（30-35%）** 3. **描述Azure上计算机视觉工作负载的特征（15-20%）** 4. **描述自然语言处理（NLP）工作负载的特征（15-20%）** 5. **描述对话式AI工作负载的特征（15-20%）** 6. **描述人工智能工作负载及其需考虑的事项（15-20%）** 课程还包括： - 识别常见AI工作负载的特征，包括预测/预测工作负载、异常检测工作负载、计算机视觉工作负载等。 - 了解负责任的AI指导原则，如公平性、可靠性和安全性、隐私和安全性、包容性、透明度和问责制的考虑因素。 关于机器学习的内容包括： - 识别常见的机器学习类型，如回归、分类和聚类情境。 - 理解核心机器学习概念，如数据集中的特征和标签、训练和验证数据集的使用、模型评估标准等。 - 研究无代码的机器学习能力，包括Azure机器学习工作室的自动化机器学习UI和设计器。 计算机视觉和NLP工作负载的内容： - 识别计算机视觉解决方案的类型，如图像分类、物体检测和OCR。 - 了解NLP工作负载的常用场景，包括关键短语提取、实体识别、情感分析等。 - 了解Azure的相关工具和服务，如计算机视觉服务、文本分析服务和对话式AI服务。 通过本课程的学习，您将全面掌握AI-900考试所需的知识和技能，帮助您顺利通过考试。
These test will help you in preparing for the AI 900 exam. All the latest changes are covered. Latest questions are added for the preparation. Go through it multiple times for preparation.AI 900 consists of following topics:Describe AI workloads and considerations (15-20%)Describe fundamental principles of machine learning on Azure (30-35%)Describe features of computer vision workloads on Azure (15-20%)Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)Describe features of conversational AI workloads on Azure (15-20%)Describe Artificial Intelligence workloads and considerations (15-20%) Identify features of common AI workloads • identify prediction/forecasting workloads • identify features of anomaly detection workloads • identify computer vision workloads • identify natural language processing or knowledge mining workloads • identify conversational AI 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 (30-35%) 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 how machine learning algorithms are used for model training • select and interpret model evaluation metrics for classification and regression Identify core tasks in creating a machine learning solution • describe common features of data ingestion and preparation • describe feature engineering and selection • describe common features of model training and evaluation • describe common features of model deployment and management Describe capabilities of no-code machine learning with Azure Machine Learning studio • automated ML UI • 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 (15-20%) 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 Text Analytics service • identify capabilities of the Language Understanding service (LUIS) • identify capabilities of the Speech service • identify capabilities of the Translator Text service Describe features of conversational AI workloads on Azure (15-20%) Identify common use cases for conversational AI • identify features and uses for webchat bots • identify common characteristics of conversational AI solutions Identify Azure services for conversational AI • identify capabilities of the QnA Maker service • identify capabilities of the Azure Bot service
