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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure | 40% | - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Scale a GPU infrastructure for different use cases - Identify hardware requirements for specific AI training task use cases - Identify facility requirements - Identify key components and considerations of a cluster of an accelerated infrastructure - Determine networking requirements for AI workloads - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Explain the purpose and benefits of a DPU in a datacenter - Identify and describe DC networking protocols and key concepts - Identify high speed DC network options and their use cases |
| Topic 2: Essential AI Knowledge | 38% | - Explain the key AI use cases and industries - Describe the NVIDIA software stack used in an AI environment - Describe the software components related to the life cycle of AI development and deployment - Compare and contrast training and inference architecture requirements and considerations - Compare and contrast GPU and CPU architectures - Explain the factors contributing to recent rapid improvements and adoption of AI - Explain the purpose and use case of various NVIDIA solutions - Differentiate the concepts of AI, machine learning, and deep learning |
| Topic 3: AI Operations | 22% | - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI data center management and monitoring essentials - Describe AI cluster orchestration and job scheduling essentials - Articulate the key measures and criteria related to monitoring GPUs |
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NEW QUESTION # 38
When should RoCE be considered to enhance network performance in a multi-node AI computing environment?
Answer: A
Explanation:
RoCE (RDMA over Converged Ethernet) enhances network performance by offloading data transport to the NIC via RDMA, bypassing CPU involvement. It's particularly valuable when high CPU utilization limits bandwidth usage, as it reduces overhead and unlocks full link capacity. While RoCE can handle storage traffic, it's less effective with high packet loss (requiring reliable networks), making CPU-bound scenarios its prime use case.
(Reference: NVIDIA Networking Documentation, Section on RoCE Benefits)
NEW QUESTION # 39
Why use NVIDIA GPUDirect Storage in an AI cluster?
Answer: B
Explanation:
NVIDIA GPUDirect Storage is used to create a direct data path between storage and GPU memory. NVIDIA' s GPUDirect Storage Overview Guide states: "GPUDirect Storage (GDS) enables a direct data path for direct memory access (DMA) transfers between GPU memory and storage, which avoids a bounce buffer through the CPU." It also says this direct path can relieve system bandwidth bottlenecks and reduce CPU latency and utilization load.
NVIDIA's GPUDirect Storage technical blog further explains that GPUDirect Storage enables "a direct data path between local or remote storage, like NVMe or NVMe over Fabric (NVMe-oF), and GPU memory." Therefore, the correct answer is C: it enables peer-to-peer memory transfers between GPUs and NVMe storage.
Why the other options are incorrect: GPUDirect Storage is not primarily about TCP/IP transmission between GPUs and CPUs. It does simplify and bypass parts of the traditional CPU-centric storage path, but the more precise answer is direct GPU-memory-to-storage transfer. It does not increase GPU clock rates.
Reference: NVIDIA GPUDirect Storage Overview Guide; NVIDIA Developer Blog - A Direct Path Between Storage and GPU Memory.
NEW QUESTION # 40
Which technology partitions a single GPU into isolated instances for parallel workloads?
Answer: B
Explanation:
MIG, or Multi-Instance GPU, is the NVIDIA technology that partitions one supported GPU into multiple isolated GPU instances. NVIDIA's MIG User Guide states: "The Multi-Instance GPU (MIG) User Guide explains how to partition supported NVIDIA GPUs into multiple isolated instances, each with dedicated compute and memory resources." It also explains that MIG enables efficient GPU utilization across multiple users or workloads with guaranteed performance.
NVIDIA AI Enterprise documentation also defines MIG as hardware-level GPU partitioning into isolated instances, each with dedicated resources. Therefore, the correct answer is MIG.
Why the other options are incorrect: vGPU virtualizes GPU access for virtual machines, but the specific technology for partitioning a single physical GPU into isolated GPU instances is MIG. NVLink is a high- speed GPU interconnect. NCCL is a communication library for multi-GPU and multi-node collective communication.
Reference: NVIDIA Multi-Instance GPU User Guide; NVIDIA AI Enterprise Glossary.
NEW QUESTION # 41
In a data center, what is the purpose and benefit of a DPU?
Answer: B
Explanation:
A Data Processing Unit (DPU) is a programmable processor that offloads, accelerates, and isolates infrastructure workloads-like networking, storage, and security-from the CPU. This enhances performance, reduces CPU overhead, and improves security by segregating tasks, benefiting AI data centers. It doesn't handle backups or physical infrastructure directly, focusing instead on compute efficiency.
NEW QUESTION # 42
You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?
Answer: C
Explanation:
Enabling GPU sharing and using NVIDIA GPU Operator with Kubernetes (C) optimizes resourceutilization by allowing flexible allocation of GPUs based on job requirements. The GPU Operator supports Multi- Instance GPU (MIG) mode on NVIDIA GPUs (e.g., A100), enabling jobs to share a single GPU when exclusive access isn't needed, while dedicating full GPUs to high-demand tasks. This dynamic scheduling, integrated with Kubernetes, balances utilization across the cluster efficiently.
* Dedicated GPU resources for all jobs(A) wastes capacity for shareable tasks, reducing efficiency.
* FIFO Scheduling(B) ignores resource demands, leading to suboptimal allocation.
* Increasing pod resource requests(D) may over-allocate resources, not addressing sharing or optimization.
NVIDIA's GPU Operator is designed for such mixed workloads (C).
NEW QUESTION # 43
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