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所在平台: Udemy |
课程主页: https://www.udemy.com/course/gcp-ml-engineer-exams-2024/
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课程名称：Google Cloud专业机器学习工程师考试 课程概述：本课程为准备参加Google Cloud专业机器学习工程师考试的个人提供练习测试。课程包含5个练习测试和1个模拟测试，每个练习测试都有100个问题，而模拟测试则有55个随机变化的问题。请注意，在使用练习测试自我检测之前，请先深入研究测试结果，仔细阅读解释，然后再进行模拟测试以测试自己。模拟测试的时间限制为2小时，每次尝试时问题和答案的顺序都会变化，模拟测试的规则与真实考试相同。 专业机器学习工程师的职责是构建、评估、生产化和优化机器学习模型，使用Google Cloud技术和经验证的模型及技术知识。机器学习工程师处理大型复杂数据集，创建可重复使用的代码，并在机器学习模型开发过程中考虑负责任的人工智能和公平性，与其他岗位紧密合作以确保基于机器学习的应用程序的长期成功。机器学习工程师需要具备强大的编程技能，以及对数据平台和分布式数据处理工具的丰富经验。他们精通模型架构、数据和机器学习管道的创建以及指标解释。机器学习工程师需了解MLOps、应用程序开发、基础设施管理、数据工程和数据治理的基本概念，使机器学习技术变得可用，并使组织内的团队能够通过训练、再训练、部署、调度、监控和改进模型，设计和创建可扩展的高效解决方案。 考试信息： - 考试时长：2小时 - 报名费用：200美元（适用时加税） - 语言：英语 - 考试形式：50-60道多项选择题和多选题 请注意：考试不会直接评估编码技能。如果您掌握基本的Python和Cloud SQL，您将能够理解任何含有代码片段的问题。
This is a course that provides practice tests for individuals preparing to take the Google Cloud Professional Machine Learning Engineer Exam. This course contains 5 practice tests and 1 mock test. each practice test has 100 questions. and the mock test has 55 questions whcih randomly changes. Please don't examine your self with the practice test. For passing the exam you should do the practice tests first, deep dive on it, read the explanation carefully and after that do the mock test to examine yourself. The Mock test has a time limit of 2 hours and everytime the you try it, the order of questions and answers will change. The rules of mock tests are as same as the real exam.Professional Machine Learning EngineerA Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.*Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.The Professional Machine Learning Engineer exam assesses your ability to:Architect low-code ML solutionsCollaborate within and across teams to manage data and modelsScale prototypes into ML modelsServe and scale modelsAutomate and orchestrate ML pipelinesMonitor ML solutionsAbout the Exam:Length: 2 hoursRegistration fee: $200 (plus tax where applicable)Languages: EnglishExam format: 50-60 multiple choice and multiple select questions
