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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure | 40% | - GPU cluster design and configuration principles - Hardware requirements for training and inference workloads - On-premises vs cloud infrastructure comparison - AI networking fundamentals and considerations - Data center power, cooling and physical requirements |
| Essential AI Knowledge | 38% | - GPU vs CPU architecture and characteristics - AI, Machine Learning, and Deep Learning concepts and differences - Purpose and benefits of DPU in data center - NVIDIA software stack and components in AI environment - Accelerated computing use cases and industry applications |
| AI Operations | 22% | - Operational best practices for NVIDIA solutions - Scaling and maintenance considerations - AI infrastructure monitoring and management basics - Cluster orchestration and job scheduling concepts |
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NEW QUESTION # 123
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: C
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 # 124
Which NVIDIA tool aids data center monitoring and management?
Answer: C
Explanation:
DCGM is the correct answer because NVIDIA DCGM stands for Data Center GPU Manager and is built for monitoring and managing NVIDIA GPUs in data center and cluster environments. NVIDIA's DCGM documentation states that DCGM provides "continuous GPU telemetry at very low performance overheads" and provides mechanisms to gather, group, and analyze data at the job level.
NVIDIA's DCGM documentation also states that DCGM-Exporter "allows users to gather GPU metrics and understand workload behavior or monitor GPUs in clusters," exposing GPU metrics for monitoring tools such as Prometheus. Therefore, DCGM is the NVIDIA tool used for data center GPU monitoring and management.
Why the other options are incorrect: TensorRT is for optimizing and running inference. Clara is NVIDIA's healthcare and medical imaging platform. Mellanox Insight is not the primary NVIDIA data center GPU monitoring and management tool referenced for GPU operations; DCGM is.
Reference: NVIDIA DCGM Documentation; NVIDIA DCGM-Exporter Documentation.
NEW QUESTION # 125
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: B
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 # 126
An AI system is deployed to detect when people enter a restricted area using multiple IP cameras. Which AI use case is being exercised?
Answer: C
Explanation:
Intelligent Video Analytics refers to AI systems that analyze video streams to detect and interpret events, such as unauthorized entry, enabling automated monitoring and actionable insights from multiple camera feeds.
NEW QUESTION # 127
A research team is deploying a deep learning model on an NVIDIA DGX A100 system. The model has high computational demands and requires efficient use of all available GPUs. During the deployment, they notice that the GPUs are underutilized, and the inter-GPU communication seems to be a bottleneck. The software stack includes TensorFlow, CUDA, NCCL, and cuDNN. Which of the following actions would most likely optimize the inter-GPU communication and improve overall GPU utilization?
Answer: B
Explanation:
Ensuring NVIDIA Collective Communications Library (NCCL) is configured correctly for optimal bandwidth utilization is the most effective action to optimize inter-GPU communication and improve utilization on an NVIDIA DGX A100. NCCL accelerates multi-GPU operations by optimizing data transfers (e.g., via NVLink, InfiniBand), critical for high-demand models. Underutilization and bottlenecks suggest suboptimal NCCL settings (e.g., topology, ring order). Option A (disable cuDNN) hampers performance, as cuDNN accelerates neural network primitives. Option B (more data parallel jobs) may worsen communication overhead. Option D (single GPU) reduces scalability. NVIDIA's DGX A100 documentation recommends NCCL tuning for distributed training efficiency.
NEW QUESTION # 128
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