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NVIDIA AI Infrastructure Sample Questions (Q162-Q167):

NEW QUESTION # 162
You are using a custom container runtime other than Docker (e.g., containerd) and need to integrate it with the NVIDIA Container Toolkit.
What command would you use to configure the NVIDIA Container Toolkit for this runtime? (Assume your runtime configuration file is located at '/etc/containerd/config.toml')

A. nvidia-ctk runtime configure
B. nvidia-ctk runtime configure -runtime=custom -config=/etc/containerd/config.tomr
C. nvidia-ctk runtime config -runtime=containerd -set-default'
D. 'nvidia-docker runtime configure -runtime=containerd'
E. nvidia-ctk runtime install -runtime-containerd'

Answer: B

Explanation:
The Anvidia-ctk runtime configure' command is used to configure the NVIDIA Container Toolkit for different container runtimes. When the runtime is not a standard one, the '-runtime=custom' option must be used, and you also provide the path to the configuration file using config=/etc/containerd/config.tomr. There is no 'install' or 'config' subcommand under 'nvidia-ctk runtime' for runtime selection.

NEW QUESTION # 163
Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the 'all reduce' operation.
What is the most likely root cause and how would you address it?

A. Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010).
B. Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using 'cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity.
C. GPU Direct RDMA is not properly configured. Check 'dmesg' for errors and ensure RDMA is enabled.
D. Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables.
E. Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version.

Answer: E

Explanation:
NCCL relies on specific CUDA versions. An incompatibility after a CUDA update is the most probable cause. Insufficient shared memory is less likely to cause a system error within NCCL. Firewall rules usually manifest as connection refused errors. Faulty network cables affect inter-node communication, not intra-node. While RDMA issues can cause problems, they typically don't present as 'unhandled system error' immediately after a CUDA update, and are more likely if RDMA was working previously.

NEW QUESTION # 164
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

A. Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.
B. Optimize the model using TensorRT.
C. Enabling CUDA graph capture to reduce kernel launch overhead.
D. Increasing the GPU's TCC (Tesla Compute Cluster) mode priority.
E. Reducing the model's memory footprint by using quantization or pruning techniques.

Answer: D

Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.

NEW QUESTION # 165
Which of the following statements regarding VXLAN (Virtual Extensible LAN) is MOST accurate in the context of data center networking for AI/ML workloads?

A. VXLAN provides Layer 2 connectivity across Layer 3 networks, enabling virtual machine mobility.
B. VXLAN primarily improves network security by encrypting all traffic.
C. VXLAN reduces network overhead compared to traditional VLANs.
D. VXLAN is only suitable for small-scale networks due to its limited scalability.
E. VXLAN requires specialized hardware and cannot be implemented in software.

Answer: A

Explanation:
VXLAN allows Layer 2 segments to be extended across Layer 3 infrastructure by encapsulating Ethernet frames within UDP packets. This enables virtual machine mobility across different subnets. VXLAN adds overhead due to encapsulation. While VXLAN can enhance security, encryption is not its primary function. It is highly scalable and can be implemented in both hardware and software.

NEW QUESTION # 166
You are deploying a cloud-native AI inference service using Kubernetes and NVIDIA GPUs. You need to ensure that GPU resources are efficiently allocated and monitored. Which of the following approaches is MOST effective for achieving this within the Kubernetes environment?

A. Deploying a dedicated monitoring agent on each node to track GPU utilization and manually adjusting pod resource requests based on these metrics.
B. Relying solely on Kubernetes' default CPU and memory resource requests and limits, assuming GPU usage will be implicitly managed.
C. Using the NVIDIA Device Plugin for Kubernetes to advertise GPU resources and utilizing resource requests and limits to schedule pods on nodes with available GPUs.
D. Manually assigning specific GPU devices to pods using hostPath volumes and environment variables.
E. Overcommitting GPU resources and relying on the Kubernetes scheduler to handle potential out-of-memory (OOM) errors.

Answer: C

Explanation:
The NVIDIA Device Plugin for Kubernetes is specifically designed to advertise GPU resources to the Kubernetes scheduler, enabling efficient allocation and utilization. Resource requests and limits ensure pods are scheduled on nodes with sufficient GPU capacity, preventing resource contention and 00M errors. Options A, C, D, and E are either ineffective, manual, or potentially lead to instability.

NEW QUESTION # 167
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