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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Accelerated computing use cases and industry applications - NVIDIA software stack and components in AI environment - Purpose and benefits of DPU in data center - GPU vs CPU architecture and characteristics - AI, Machine Learning, and Deep Learning concepts and differences |
| Topic 2: AI Infrastructure | 40% | - GPU cluster design and configuration principles - Hardware requirements for training and inference workloads - Data center power, cooling and physical requirements - On-premises vs cloud infrastructure comparison - AI networking fundamentals and considerations |
| Topic 3: AI Operations | 22% | - AI infrastructure monitoring and management basics - Operational best practices for NVIDIA solutions - Scaling and maintenance considerations - Cluster orchestration and job scheduling concepts |
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問題 #115
Which phenomenon explains performance degradation when adding many irrelevant features?
答案：A
解題說明：
High-dimensional spaces dilute distance metrics and require exponentially more data.
問題 #116
Which NVIDIA product is used for data preparation in an AI workflow?
答案：B
解題說明：
NVIDIA identifies RAPIDS as the correct product for data preparation. NVIDIA AI Enterprise documentation describes NVIDIA RAPIDS as "GPU-accelerated data science libraries for data preparation, machine learning, and graph analytics." RAPIDS is therefore the correct answer because it accelerates data science and data preparation workflows on GPUs. DOCA is primarily for data center infrastructure and DPU software development, while DLSS is an AI-powered graphics rendering technology, not a data-preparation product for AI workflows.
Reference: NVIDIA AI Enterprise Application Layer Software documentation.
問題 #117
What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?
答案：B
解題說明：
NVIDIA GPUDirect RDMA allows network adapters to directly access GPU memory, bypassing the CPU and operating system kernel. This accelerates data transfers between GPUs and CPUs (or other devices), reducing latency and CPU overhead in AI workflows, such as multi-node training. It doesn't focus on power efficiency or unsynchronized memory sharing, making faster transfers its key benefit.
問題 #118
Which two components are included in GPU Operator? (Choose two.)
答案：B,C
解題說明：
The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes environments. It includes two key components: GPU drivers, which provide the necessary software to interface with NVIDIA GPUs, and the NVIDIA Data Center GPU Manager (DCGM), which offers health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and TensorFlow are separate AI development tools, not part of the GPU Operator, which focuses on infrastructure rather than application layers.
問題 #119
Your organization has deployed a large-scale AI data center with multiple GPUs running complex deep learning workloads. You've noticed fluctuating performance and increasing energy consumption across several nodes. You need to optimize the data center's operation and improve energy efficiency while ensuring high performance. Which of the following actions should you prioritize to achieve optimized AI data center management and maintain efficient energyconsumption?
答案：C
解題說明：
Implementing GPU workload scheduling based on real-time performance metrics is the priority action to optimize AI data center management and improve energy efficiency while maintaining performance. Using tools like NVIDIA DCGM, this approach monitors metrics (e.g., power usage, utilization) and schedules workloads to balance load, reduce idle time, and leverage power-saving features (e.g., GPU Boost). This aligns with NVIDIA's "AI Infrastructure and Operations Fundamentals" for energy-efficient GPU management without sacrificing throughput.
Disabling power management (A) increases consumption unnecessarily. Adding GPUs (C) raises costs without addressing efficiency. More cooling (D) mitigates symptoms, not root causes. NVIDIA prioritizes dynamic scheduling for optimization.
問題 #120
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