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NVIDIA AI Infrastructure Sample Questions (Q15-Q20):
NEW QUESTION # 15
You've installed the NGC CLI, but when you run 'ngc registry model list' you get an error indicating authentication failure. You're sure your API key is correct. What could be the cause, and how would you diagnose this?
- A. Your organization might be behind a proxy that is blocking the NGC CLI from accessing the internet. Configure the proxy settings for the NGC CLI.
- B. Your account lacks the necessary permissions to access the NGC registry. Contact your NVIDIA administrator.
- C. The environment variables 'NGC API_KEY or 'NGC CLI_API_KEY are set incorrectly or not set at all. Verify and set them correctly.
- D. The host machine's clock is not synchronized, causing authentication issues. Synchronize the clock using 'ntpd' or 'chronyd'.
- E. The NGC CLI version is outdated. Upgrade to the latest version using 'pip install -upgrade nvidia-cli'.
Answer: A,B,D
Explanation:
Authentication failures can be caused by proxy issues (C), insufficient account permissions (D), or clock synchronization problems (E). While an outdated CLI version (A) could potentially cause issues, it's less likely to manifest as an authentication failure. Environment variables (B) are generally not the primary source of error when using 'ngc config set' to configure authentication.
NEW QUESTION # 16
A data scientist reports that training performance on a DGX A100 server has significantly degraded over the past week. 'nvidia-smi' shows all GPUs functioning, but 'nvprof' reveals substantially increased 'cudaMemcpy' times. What is the MOST likely bottleneck?
- A. The GPUs are overheating, causing thermal throttling and slower memory transfers.
- B. The CPU is heavily loaded, causing contention for system memory bandwidth.
- C. The storage system is slow, delaying data loading and preprocessing.
- D. The NVLink connections between GPUs are failing, forcing data transfers through PCle.
- E. The PCle bus is saturated, limiting data transfer speeds between the CPU and GPUs.
Answer: B
Explanation:
Increased ScudaMemcpy' times indicate a bottleneck in data transfer between the CPU and GPIJs or within the GPU memory itself. While PCle saturation or failing NVLink connections could contribute, a heavily loaded CPU is often the primary culprit. CPU-bound preprocessing tasks or general system load can create contention for system memory, slowing down data movement to the GPUs. The NVLink being used is less likely because 'nvidia-smi' would likely report errors or lower speeds between the GPUs
NEW QUESTION # 17
After ClusterKit reports "GPU-Host latency exceeds threshold," which NVIDIA diagnostic tool should be used to isolate hardware faults?
- A. Re-run ClusterKit with --stress=gpu -Y 60 to extend test duration
- B. ib_write_bw to measure InfiniBand bandwidth between nodes
- C. DCGM Diags dcgmi diag -r 2
- D. nvidia-smi topo -m to inspect GPU topology connections
Answer: D
Explanation:
"GPU-Host latency" issues in NVIDIA DGX or HGX systems are frequently caused by incorrect PCIe affinity or sub-optimal NUMA (Non-Uniform Memory Access) mapping. If a GPU is forced to communicate with a CPU core or an HCA that is not on its local PCIe switch/root complex, latency increases significantly as data must cross the QPI/UPI inter-processor links. The command nvidia-smi topo -m provides a detailed matrix of the system's internal topology, showing how GPUs, CPUs, and NICs are connected. It identifies whether the connection is via a single PCIe switch (PIX), multiple switches (PXB), or across the CPU (SYS).
By inspecting this map, an administrator can identify if a software process is pinned to the wrong NUMA node or if a hardware path is unexpectedly degraded. While DCGM (Option C) is good for checking component health, it doesn't map the logical-to-physical affinity paths that cause specific latency "threshold" warnings.
NEW QUESTION # 18
You are configuring a switch port connected to a host in an NCP-AII environment. The host is running RoCEv2. To optimize performance and prevent packet loss, which flow control mechanism should you enable on the switch port?
- A. Simple Network Management Protocol (SNMP).
- B. Spanning Tree Protocol (STP).
- C. TCP flow control.
- D. Priority Flow Control (PFC) or 802.1 Qbb, specifically for the traffic class associated with RoCEv2.
- E. None; flow control is not needed with RoCEv2.
Answer: D
Explanation:
Priority Flow Control (PFC), also known as 802.1Qbb, is the appropriate flow control mechanism for RoCEv2. RoCEv2 is a lossless Ethernet protocol, and PFC allows you to enable flow control on specific traffic classes, preventing packet loss in congested situations. By enabling PFC specifically for the traffic class carrying RoCEv2 traffic, you ensure that high-priority A1 training data is delivered reliably without being affected by congestion on other parts of the network.
NEW QUESTION # 19
You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?
- A. Reduce the ambient temperature of the data center.
- B. Increase the fan speed of the GPU cooler using 'nvidia-smi --fan'.
- C. Lower the GPU power limit using 'nvidia-smi -power-limit*.
- D. Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation.
- E. Update the NVIDIA drivers to the latest version.
Answer: B,D
Explanation:
Re-seating the GPU (D) ensures a proper connection between the GPU and the motherboard, which is crucial for effective heat dissipation. Increasing fan speed (E) can directly improve cooling. Lowering the power limit (B) reduces temperature but also reduces performance. Updating drivers (C) may help in some cases, but it is less likely to solve a thermal throttling problem. Lowering the ambient temperature (A) is generally beneficial but might not be specific enough to fix the overheating issue on a single server.
NEW QUESTION # 20
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