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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Essential AI Knowledge | 38% | - Differentiate the concepts of AI, machine learning, and deep learning - Describe the software components related to the life cycle of AI development and deployment - Explain the purpose and use case of various NVIDIA solutions - Explain the factors contributing to recent rapid improvements and adoption of AI - Compare and contrast training and inference architecture requirements and considerations - Explain the key AI use cases and industries - Compare and contrast GPU and CPU architectures - Describe the NVIDIA software stack used in an AI environment |
| AI Infrastructure | 40% | - Explain the purpose and benefits of a DPU in a datacenter - Determine networking requirements for AI workloads - Identify hardware requirements for specific AI training task use cases - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Identify facility requirements - Identify high speed DC network options and their use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Scale a GPU infrastructure for different use cases - Identify and describe DC networking protocols and key concepts |
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121. Frage
In an AI cluster, what is the importance of using Slurm?
Antwort: B
Begründung:
Slurm (Simple Linux Utility for Resource Management) is a workload manager critical for AI clusters, handling job scheduling and resource allocation. It ensures tasks are assigned to available GPUs/CPUs efficiently, supporting scalable training and inference. It doesn't manage storage, perform training, or interconnect nodes--those are separate functions.
122. Frage
When should RoCE be considered to enhance network performance in a multi-node AI computing environment?
Antwort: B
Begründung:
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)
123. Frage
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?
Antwort: C
Begründung:
Ensuring that GPU memory is not overcommitted among VMs is a key factor for efficient and scalable performance in a virtualized GPU-accelerated infrastructure. NVIDIA's vGPU technology allows multiple VMs to share a GPU, but overcommitting memory (allocating more than physically available) causes contention, degrading performance. Proper memory allocation, as outlined in NVIDIA's vGPU documentation, ensures each VM has sufficient resources for AI workloads. Option A (more CPU) doesn't address GPU bottlenecks. Option C (network bandwidth) aids communication, not GPU efficiency. Option D (nested virtualization) adds complexity without direct benefit. NVIDIA emphasizes memory management for virtualization success.
124. Frage
Which feature of RDMA reduces CPU utilization and lowers latency?
Antwort: B
Begründung:
Remote Direct Memory Access (RDMA) reduces CPU utilization and latency through network adapters with hardware offloading. These adapters handle data transfers directly between memory locations, bypassing CPU-intensive operations like memory copies and protocol processing. Larger buffers and software like Magnum I/O may enhance performance, but hardware offloading is the core RDMA feature delivering these benefits.
(Reference: NVIDIA Networking Documentation, Section on RDMA Offloading)
125. Frage
Which of the following aspects have led to an increase in the adoption of AI? (Choose two.)
Antwort: C,D
Begründung:
The surge in AI adoption is driven by two key enablers: high-powered GPUs and large amounts of data. High- powered GPUs provide the massive parallel compute capabilities necessary to train complex AI models, particularly deep neural networks, by processing numerous operations simultaneously, significantly reducing training times. Simultaneously, the availability of large datasets-spanning text, images, and other modalities-provides the raw material that modern AI algorithms, especially data-hungry deep learning models, require to learn patterns and make accurate predictions. While Moore's Law (the doubling of transistor counts) has historically aided computing, its impact has slowed, and rule-based machine learning has largely been supplanted by data-driven approaches.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on AI Adoption Drivers)
126. Frage
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