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NEW QUESTION # 71
Your AI team is deploying a multi-stage pipeline in a Kubernetes-managed GPU cluster, where some jobs are dependent on the completion of others. What is the most efficient way to ensure that these job dependencies are respected during scheduling and execution?
Answer: C
Explanation:
Using Kubernetes Jobs with Directed Acyclic Graph (DAG) scheduling is the most efficient way to ensure job dependencies are respected in a multi-stage pipeline on a GPU cluster. Kubernetes Jobs allow you to define tasks that run to completion, and integrating a DAG workflow (e.g., via tools like Argo Workflows or Kubeflow Pipelines) enables you to specify dependencies explicitly. This ensures that dependent jobs only start after their prerequisites finish, automating the process and optimizing resource use on NVIDIA GPUs.
Increasing job priority (A) affects scheduling order but does not enforce dependencies. Deploying all jobs concurrently with pod anti-affinity (C) prevents resource contention but ignores execution order. Manual monitoring (D) is inefficient and error-prone. NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals" recommend DAG-based scheduling for dependency management in Kubernetes GPU clusters.
NEW QUESTION # 72
An organization is deploying a large-scale AI model across multiple NVIDIA GPUs in a data center. The model training requires extensive GPU-to-GPU communication to exchange gradients. Which of the following networking technologies is most appropriate for minimizing communication latency and maximizing bandwidth between GPUs?
Answer: D
Explanation:
InfiniBand is the most appropriate networking technology for minimizing communication latencyand maximizing bandwidth between NVIDIA GPUs during large-scale AI model training. InfiniBand offers ultra- low latency and high throughput (up to 200 Gb/s or more), supporting RDMA for direct GPU-to-GPU data transfer, which is critical for exchanging gradients in distributed training. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure for Enterprise" documentation recommend InfiniBand for its performance in GPU clusters like DGX systems.
Ethernet (B) is slower and higher-latency, even with high-speed variants. Wi-Fi (C) is unsuitable for data center performance needs. Fibre Channel (D) is storage-focused, not optimized for GPU communication.
InfiniBand is NVIDIA's standard for AI training networks.
NEW QUESTION # 73
An AI research team is working on a large-scale natural language processing (NLP) model that requires both data preprocessing and training across multiple GPUs. They need to ensure that the GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies should they use?
Answer: D
Explanation:
NVIDIA DALI (Data Loading Library) and NVIDIA NCCL (Collective Communications Library) are the best combination for efficient GPU use in NLP model training. DALI accelerates data preprocessing (e.g., tokenization) on GPUs, reducing CPU bottlenecks, while NCCL optimizes inter-GPU communication for distributed training, minimizing latency and maximizing utilization. Option A (TensorRT) focuses on inference, not training. Option B (DeepStream) targets video analytics. Option D (cuDNN, NGC) supports neural ops and model access but lacks preprocessing/communication focus. NVIDIA's NLP workflows recommend DALI and NCCL for efficiency.
NEW QUESTION # 74
What NVIDIA tool should a data center administrator use to monitor NVIDIA GPUs?
Answer: A
Explanation:
The NVIDIA Data Center GPU Manager (DCGM) is the recommended tool for data center administrators to monitor NVIDIA GPUs. It provides real-time health monitoring, telemetry (e.g., utilization, temperature), and diagnostics, tailored for large-scale deployments. NetQ focuses on network monitoring, and there's no "NVIDIA System Monitor" in this context, making DCGM the correct choice. (Note: The document incorrectly lists D; C is intended.)
NEW QUESTION # 75
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 # 76
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