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NVIDIA AI Operations Sample Questions (Q140-Q145):

NEW QUESTION # 140
You are troubleshooting a distributed deep learning training job that utilizes GPUDirect Storage for data loading and CUDA-Aware MPI with GPUDirect RDMA for inter-GPU communication. The training process is significantly slower than expected, and you suspect a bottleneck in the data pipeline. You've used 'nvprof and determined that the data loading phase is taking an unusually long time. Which of the following steps would be the MOST effective next step in diagnosing the issue? SELECT TWO.

A. Verify that the storage devices being used support GPUDirect Storage.
B. Profile the network bandwidth
C. Monitor the CPU utilization during the data loading phase.
D. Check the PCIe bandwidth utilization between the storage devices and the GPUs.
E. Examine the NCCL logs for communication errors.

Answer: A,D

Explanation:
Explanation: Given that 'nvprof indicates a slow data loading phase, the most effective next steps are to: 1. Verify that the storage devices support GPUDirect Storage: If the storage devices do not properly support GPUDirect Storage, the data will likely be transferred through the CPU, negating the performance benefits. 2. Check the PCIe bandwidth utilization: Even if the storage devices support GPUDirect Storage, the PCIe link between the storage devices and the GPUs may be saturated, limiting the data transfer rate. High CPU utilization (A) might indicate that GPUDirect Storage is not working correctly, but verifying storage support is more direct. NCCL logs (D) are more relevant for inter-GPU communication issues. Network bandwidth (E) impacts inter-GPU communication, but the problem is data loading.

NEW QUESTION # 141
You are deploying BCM on a Kubernetes cluster that utilizes a custom ingress controller What configuration changes might be necessary to ensure external access to the BCM web interface?

A. Configure the ingress controller to forward traffic to the BCM service on the appropriate port (typically 3000).
B. Disable the default ingress controller and use the custom ingress controller exclusively.
C. Modify the BCM service type to 'LoadBalancer'.
D. Create a custom resource definition (CRD) for BCM in the ingress controller.
E. Update the BCM Helm chart to automatically configure the ingress controller.

Answer: A

Explanation:
The primary configuration change required is to configure the ingress controller to forward traffic to the BCM service. This typically involves creating an ingress resource that defines the hostname and path for accessing the BCM web interface and maps it to the BCM service on the correct port (usually 3000). Changing the service type to 'LoadBalancer' might work, but it's less flexible and might not be compatible with all ingress controllers. Custom CRDs and disabling the default controller are generally unnecessary.

NEW QUESTION # 142
You've noticed consistently high GPU utilization but low overall throughput in your AI inference service. You suspect that a CUDA kernel is not efficiently utilizing the GPU's resources. Which profiling tool would provide the MOST detailed insights into kernel-level performance?

A. DCGM
B. nvidia-smi'
C. NVIDIA Nsight Systems
D. 'top'
E. 'vmstat'

Answer: C

Explanation:
NVIDIA Nsight Systems (and its successor Nsight Compute for kernel-level analysis) is specifically designed for profiling CUDA kernels. It provides detailed information on kernel execution time, memory access patterns, and instruction-level performance, allowing you to identify inefficiencies. 'nvidia-smr and DCGM provide high-level GPU monitoring, while 'top' and 'vmstat' are system-level tools.

NEW QUESTION # 143
A user submits a Slurm job script with the following options:

Assuming each node has 4 GPUs, how many GPU resources will be allocated to this job across the entire cluster?

A. 0
B. 1
C. 2
D. 3
E. 4

Answer: E

Explanation:
The job requests 2 nodes (nodes=2) and one GPU per node Therefore, a total of 2 GPUs (2 nodes 1 GPU/node) will be allocated to the job.

NEW QUESTION # 144
What is the primary benefit of using NVIDIA MIG in a multi-tenant environment?

A. Decreased memory usage.
B. Simplified container deployment.
C. Guaranteed isolation and resource allocation for each tenant.
D. Increased network bandwidth.
E. Improved CPU performance.

Answer: C

Explanation:
MIG's primary benefit is to provide guaranteed isolation and resource allocation for each tenant in a multi-tenant environment. This ensures that each tenant has dedicated GPU resources and that their workloads do not interfere with each other.

NEW QUESTION # 145
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