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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nvidia-certified-professional-ai-operations-practice-tests/
课程评论：没有评论
课程名称：NVIDIA认证专业人员：AI操作实践测试 课程概述：在现代数据中心，NVIDIA的硬件和软件堆栈在推动人工智能（AI）和高性能计算（HPC）工作负载方面发挥着至关重要的作用。本实践测试课程专为帮助您准备NVIDIA AI操作认证而设计，确保您能够有效监控、排除故障并优化大规模AI基础设施。在本课程中，您将学习如何管理Slurm集群，配置Kubernetes以实现基于GPU的调度，并管理MIG分区以最大化多租户和资源利用率。您还将了解如何安装和配置基础命令管理器（BCM），集成高级功能，如Magnum IO数据平面加速，并利用BlueField DPUs上的DOCA服务进行网络和存储卸载。通过真实场景和深入的实践问题，您将获得识别和解决Docker容器、HPC调度层和多GPU系统中复杂问题的信心。此外，您还将学习AI验证HPC存储性能的数据中心架构基础知识，确保充足的电力和冷却，并管理大型训练集的并发性。无论您是有志于成为数据中心工程师的新人，还是正在更新技能的资深专业人士，这些实践考试不仅将为您准备NVIDIA认证专业人员：AI操作考试，还将使您能够在现实环境中维护和优化先进的AI环境。
In modern data centers, NVIDIA hardware and software stacks play an essential role in powering AI and HPC workloads. This practice tests course is specifically designed to help you prepare for the NVIDIA AI Operations certification, ensuring you can effectively monitor, troubleshoot, and optimize AI infrastructure at scale.Throughout this course, you'll explore how to administer Slurm clusters, configure Kubernetes for GPU-based scheduling, and manage MIG partitions to maximize multi-tenancy and resource utilization. You'll also learn about installing and configuring Base Command Manager (BCM), integrating advanced features like Magnum IO data-plane acceleration, and leveraging DOCA services on BlueField DPUs for network and storage offloads. By walking through real-world scenarios and in-depth practice questions, you'll gain the confidence to identify and resolve complex issues across Docker containers, HPC scheduling layers, and multi-GPU systems.Additionally, you'll learn the fundamentals of data center architecture for AI-validating HPC storage performance, ensuring sufficient power and cooling, and managing concurrency for large training sets. Whether you're an aspiring data center engineer or a seasoned professional updating your skills, these practice exams will not only prepare you for the NVIDIA-Certified Professional: AI Operations exam but also empower you to maintain and optimize advanced AI environments in real-world contexts.
