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NVIDIA AI Operations Sample Questions (Q80-Q85):

NEW QUESTION # 80
Your organization is deploying an AI workload that requires high-throughput access to shared storage across multiple servers. The workload involves both training and inference tasks that need fast read and write speeds.
Which storage architecture would best support this AI workload?

A. A high-performance shared storage system that supports both high read and write IO performance.
B. Use local storage on each server to minimize network traffic between nodes.
C. Use SSD-based shared storage systems to save costs while scaling up storage capacity.
D. Prioritize write performance over read performance since training tasks dominate AI workflows.

Answer: A

Explanation:
For AI workloads involving both training and inference across multiple servers, a high- performance shared storage system that supports both high read and write I/O performance is essential. This ensures fast data access and efficient coordination between distributed compute nodes, preventing bottlenecks in data throughput. Local storage may minimize network traffic but lacks the necessary data sharing and coordination. Prioritizing only write performance neglects inference workload needs, and cost-saving SSD options might not deliver the required performance at scale. Hence, option C is the best choice for balanced, high-throughput AI workloads.

NEW QUESTION # 81
When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

A. 'bcmResources.limits'
B. 'deployment.resources.limits'
C. bcm.resources.limits'
D. resources.limits'
E. 'resources.bcmLimits'

Answer: D

Explanation:
The 'resources.limits' parameter in the Helm chart is the standard way to specify resource limits (CPU and memory) for pods deployed via Helm. This parameter is used to configure the resource requests and limits for the BCM pods.

NEW QUESTION # 82
You are deploying a distributed AI training workload across multiple geographically separated data centers. Which network architecture would BEST minimize latency for inter-node communication?

A. A wireless mesh network.
B. A VPN (Virtual Private Network) over the public internet.
C. A dedicated private network with DWDM (Dense Wavelength Division Multiplexing) and optimized routing.
D. A content delivery network (CDN).
E. Public internet with standard TCP/IP routing.

Answer: C

Explanation:
For geographically distributed training, minimizing latency is paramount. A dedicated private network with DWDM and optimized routing provides the lowest latency and most predictable performance compared to the public internet, VPNs, or CDNs. DWDM maximizes the bandwidth over fiber optic cables. A CDN is designed for content delivery, not low-latency communication between training nodes.

NEW QUESTION # 83
Which command line utility can be used to verify the proper functioning of GPUDirect RDMA between two GPUs on different nodes?

A. nvidia-smi'
B. rocminfo'
C. Ispci'
D. 'cuda-memcheck'
E. 'ibv devinfo'

Answer: E

Explanation:
'ibv_devinfo' is a command-line utility (part of the InfiniBand Verbs library) that provides information about RDMA devices and their capabilities. This includes verifying that RDMA is enabled and configured correctly, which is essential for GPUDirect RDMA. 'nvidia-smi' monitors GPU status. 'rocminfo' is for AMD GPUs. 'cuda-memcheck' is for CUDA memory errors. 'Ispci' lists PCI devices, but it doesn't specifically verify RDMA functionality.

NEW QUESTION # 84
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?

A. nvprof
B. nvidia-container-cli
C. nvcc
D. nvidia-cuda-smi
E. nvidia-smi

Answer: E

Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.

NEW QUESTION # 85
......

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