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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Purpose and benefits of DPU in data center - AI, Machine Learning, and Deep Learning concepts and differences - NVIDIA software stack and components in AI environment - Accelerated computing use cases and industry applications - GPU vs CPU architecture and characteristics |
| Topic 2: AI Operations | 22% | - Cluster orchestration and job scheduling concepts - Scaling and maintenance considerations - Operational best practices for NVIDIA solutions - AI infrastructure monitoring and management basics |
| Topic 3: AI Infrastructure | 40% | - Hardware requirements for training and inference workloads - Data center power, cooling and physical requirements - GPU cluster design and configuration principles - AI networking fundamentals and considerations - On-premises vs cloud infrastructure comparison |
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NEW QUESTION # 43
When using an InfiniBand network for an AI infrastructure, which software component is necessary for the fabric to function?
Answer: C
Explanation:
OpenSM (Open Subnet Manager) is essential for InfiniBand networks, managing the fabric by discovering topology, configuring switches and host channel adapters (HCAs), and handling routing. Without it, the fabric cannot operate. Verbs is an API for RDMA, and MPI is a communication protocol, but OpenSM is the critical software component for functionality.
(Reference: NVIDIA Networking Documentation, Section on InfiniBand Subnet Management)
NEW QUESTION # 44
You are managing an AI data center where multiple GPUs are orchestrated across a large cluster to run various deep learning tasks. Which of the following actions best describes an efficient approach to cluster orchestration in this environment?
Answer: B
Explanation:
Implementing a Kubernetes-based orchestration system to dynamically allocate GPU resources based on workload demands is the most efficient approach for managing a multi-GPU AI cluster. Kubernetes, enhanced by NVIDIA's GPU Operator, supports dynamic scheduling, resource allocation, and scaling for deep learning tasks, ensuring optimal GPU utilization and adaptability.Option A (round-robin) ignores workload specifics, leading to inefficiency. Option B (least power) sacrifices performance for minor cost savings. Option D (most powerful GPU) creates bottlenecks and underutilizes other GPUs. NVIDIA's documentation on Kubernetes integration highlights its effectiveness for AI cluster orchestration.
NEW QUESTION # 45
How many 1 Gb Ethernet in-band network connections are in a DGX H100 system?
Answer: A
Explanation:
The DGX H100 system uses high-speed NVIDIA ConnectX-7 QSFP56 ports (supporting 10 GbE and above) for in-band management and storage traffic, with no 1 Gb Ethernet interfaces allocated to in-band networks. A single 1 GbE RJ45 port exists, but it's reserved for out-of-band Baseboard Management Controller (BMC) tasks, not in-band connectivity.
(Reference: NVIDIA DGX H100 System Documentation, Networking Section)
NEW QUESTION # 46
When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?
Answer: D
Explanation:
Using GPU sharing technologies like NVIDIA GRID (A) is a critical factor for optimal performance in a virtualized AI infrastructure. NVIDIA GRID (or its successor, NVIDIA vGPU) enables dynamic allocation of GPU resources across virtual machines (VMs), allowing multiple AI workloads to share a physical GPU efficiently. This ensures high performance by providing each VM with direct GPU acceleration tailored to its needs, while maximizing resource utilization-keyfor AI tasks like training or inference.
* Assigning more storage(B) improves I/O but doesn't directly enhance GPU performance for compute- heavy AI workloads.
* Increasing virtual CPUs(C) boosts CPU capacity, but AI workloads rely primarily on GPU acceleration, not vCPUs.
* Disabling hyper-threading(D) might reduce CPU contention but doesn't address GPU virtualization needs.
NVIDIA's virtualization documentation emphasizes vGPU/GRID for AI performance (A).
NEW QUESTION # 47
What enables moving data between GPU memory and local or remote storage without using the CPU?
Answer: D
Explanation:
NVIDIA GPUDirect Storage enables direct data paths between GPU memory and local or remote storage (e.g., NVMe over fabrics), bypassing the CPU and host memory. This maximizes throughput and minimizes latency in AI data pipelines. NVLink connects GPUs, GPUDirect P2P facilitates GPU-to- GPU transfers, and InfiniBand is a network fabric, but only GPUDirect Storage targets storage access.
NEW QUESTION # 48
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