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NEW QUESTION # 62
An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA's recommended procedure?
Answer: B
Explanation:
The correct command is sudo nvsm stress-test --force. NVIDIA recommends using NVSM for the DGX pre- flight stress test before putting a DGX H100 system into production or after servicing. The documented NVSM stress test can run checks across supported components, including GPUs, CPU, memory, and storage, and the recommended command for all supported components is sudo nvsm stress-test --force. nvidia-smi -q provides detailed GPU information, but it does not execute a full platform stress test. The Linux stress command can load CPU and I/O subsystems, but it is generic and does not validate the DGX platform using NVIDIA's health model. gpu_burn may stress GPUs, but it does not cover CPU, system memory, storage, and DGX-specific platform checks in the recommended way. During server bring-up, NVSM is preferred because it understands DGX hardware components and can identify platform health issues before the node is released to production workloads.
NEW QUESTION # 63
A system engineer needs to set the vGPU scheduling behavior for all GPUs to share the scheduling equally with the default time slice length. What command should be used?
Answer: C
Explanation:
The RmPVMRL=0x00 setting configures the NVIDIA vGPU scheduler to use equal-share scheduling with the default time slice length. The esxcli system module parameters set -m nvidia command is the correct ESXi command format for setting NVIDIA kernel module parameters globally for the GPUs.
NEW QUESTION # 64
You have created MIG instances on an A100 GPU and want to dynamically adjust their size based on workload demands. Which of the following methods is the most appropriate for automatically resizing MIG instances in response to changing resource requirements?
Answer: B
Explanation:
Explanation: Dynamically resizing MIG instances requires a mechanism that can automatically adjust the underlying GPU partitioning based on workload demands. The most appropriate method is leveraging a GPU virtualization platform (C) that offers dynamic resource allocation and integrates with MIG. These platforms can monitor resource utilization and automatically resize MIG instances accordingly. Manually resizing (A) is impractical for dynamic adjustments. Kubernetes resource quotas (B) control container resource limits, not the underlying MIG configuration. CUDA MPS (D) allows sharing a single GPU but doesn't resize MIG instances. Adjusting application code (E) doesn't address the need for dynamic MIG resizing.
NEW QUESTION # 65
A server with 8 NVIDIAAIOO GPUs is experiencing an unexpected shutdown under heavy load. The IPMI logs show a 'Power Supply Deasserted' event immediately preceding the shutdown. After replacing the PSU, the issue persists. What is the MOST likely cause of the continued shutdowns?
Answer: C
Explanation:
The 'Power Supply Deasserted' event, even after replacing the PSIJ, strongly suggests that overcurrent protection (OCP) is being triggered. OCP is a safety mechanism that shuts down the PSU if it detects excessive current draw. This is particularly likely with multiple high- power GPUs, as the inrush current during startup can momentarily exceed the PSU's capacity. A driver issue or insufficient memory is less likely to cause this specific event.
NEW QUESTION # 66
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can't immediately improve the data center cooling. What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?
Answer: B,C
Explanation:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.
NEW QUESTION # 67
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