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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q57-Q62):
NEW QUESTION # 57
Your organization is setting up an AI infrastructure to support a range of AI workloads, including data processing, model training, and inference. The infrastructure needs to be scalable, support distributed training, and handle large datasets efficiently. Which NVIDIA solution would be most suitable for managing and orchestrating this AI infrastructure?
- A. NVIDIA RAPIDS
- B. NVIDIA DGX Systems
- C. NVIDIA TensorRT
- D. NVIDIA DeepOps
Answer: D
Explanation:
NVIDIA DeepOps is the most suitable solution for managing and orchestrating an AI infrastructure that supports scalable, distributed training and efficient handling of large datasets. DeepOps is an open-source toolkit for deploying and managing GPU clusters (e.g., DGX systems) with orchestration platforms like Kubernetes and Slurm. It provides scripts and configurations to automate setup, scaling, and operation of AI workloads, ensuring flexibility and efficiency, as outlined in NVIDIA's "DeepOps Documentation." TensorRT (B) optimizes inference, not infrastructure management. RAPIDS (C) accelerates data processing but lacks orchestration features. DGX Systems (D) are hardware platforms, not management tools. DeepOps aligns with NVIDIA's infrastructure management strategy.
NEW QUESTION # 58
You are tasked with virtualizing the GPU resources in a multi-tenant AI infrastructure where different teams need isolated access to GPU resources. Which approach is most suitable for ensuring efficient resource sharing while maintaining isolation between tenants?
- A. Implementing CPU-based virtualization
- B. Using GPU passthrough for each tenant
- C. Deploying containers without GPU isolation
- D. NVIDIA vGPU (Virtual GPU) Technology
Answer: D
Explanation:
NVIDIA vGPU (Virtual GPU) Technology is the most suitable approach for virtualizing GPU resources in a multi-tenant AI infrastructure while ensuring efficient sharing and isolation. vGPU allows multiple VMs to share a physical GPU with dedicated memory and compute slices, providing isolation via virtualization while maximizing resource utilization. NVIDIA's vGPU documentation highlights its use in enterprise environments for secure, scalable AI workloads. Option B (GPU passthrough) dedicates entire GPUs, reducing sharing efficiency. Option C (containers without isolation) risks resource contention. Option D (CPU-based virtualization) excludes GPU acceleration. vGPU is NVIDIA's recommended solution for this scenario.
NEW QUESTION # 59
In an AI infrastructure setup using NVIDIA GPUs across multiple nodes, you notice that the inter-node communication latency is higher than expected during distributed training. Which networking feature or protocol is most likely responsible for reducing latency in this scenario?
- A. VLAN segmentation
- B. TCP/IP over Ethernet
- C. InfiniBand with RDMA (Remote Direct Memory Access)
- D. Network Address Translation (NAT)
Answer: C
Explanation:
InfiniBand with RDMA (Remote Direct Memory Access) is the most effective networking feature for reducing inter-node communication latency in distributed training on NVIDIA GPUs. InfiniBand, paired with RDMA, enables direct memory access between nodes, bypassing CPU overhead and achieving ultra-low latency and high bandwidth (e.g., 200 Gb/s), critical for GPU-to-GPU data transfers via NVLink or NCCL.
Option A (NAT) manages addressing, not latency. Option B (TCP/IP over Ethernet) has higher overhead than InfiniBand. Option D (VLAN segmentation) aids isolation, not speed. NVIDIA's DGX and cluster documentation recommend InfiniBand for distributed AI workloads.
NEW QUESTION # 60
In managing an AI data center, you need to ensure continuous optimal performance and quickly respond to any potential issues. Which monitoring tool or approach would best suit the need to monitor GPU health, usage, and performance metrics across all deployed AI workloads?
- A. Nagios Monitoring System
- B. NVIDIA DCGM (Data Center GPU Manager)
- C. Prometheus with Node Exporter
- D. Splunk
Answer: B
Explanation:
NVIDIA DCGM (Data Center GPU Manager) is the best tool for monitoring GPU health, usage, and performance metrics across AI workloads in a data center. DCGM provides real-time insights into GPU- specific metrics (e.g., memory usage, utilization, power, errors), designed for NVIDIA GPUs in enterprise environments like DGX clusters. It integrates with orchestration tools (e.g., Kubernetes) and supports proactive issue detection, as detailed in NVIDIA's "DCGM User Guide." Nagios (A) and Prometheus (B) are general-purpose monitoring tools, lacking GPU-specific depth. Splunk (C) is a log analytics platform, not optimized for GPU monitoring. DCGM is NVIDIA's dedicated solution for AI data center management.
NEW QUESTION # 61
You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency. Which of the following networking technologies is most suitable for this scenario?
- A. Wi-Fi 6
- B. Ethernet (1 Gbps)
- C. InfiniBand
- D. Fiber Channel
Answer: C
Explanation:
InfiniBand (C) is the most suitable networking technology for scalable, low-latency data transfer in multi- node GPU training. It offers high throughput (up to 400 Gbps) and ultra-low latency (<1 µs), ideal for synchronizing gradients and weights across nodes using NVIDIA NCCL. InfiniBand's RDMA (Remote Direct Memory Access) further enhances efficiency by bypassing CPU overhead, critical for distributed deep learning.
* Wi-Fi 6(A) lacks the reliability and bandwidth (max ~10 Gbps) for training clusters.
* Fiber Channel(B) is for storage, not compute node interconnects.
* Ethernet (1 Gbps)(D) is too slow for large-scale AI training demands.
NVIDIA's DGX systems use InfiniBand for this purpose (C).
NEW QUESTION # 62
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