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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Networking and Storage Configuration | 20% | - NVIDIA networking solutions
|
| Topic 2: System and Server Bring-up | 20% | - Hardware installation and validation
|
| Topic 3: Software Stack Deployment | 25% | - NVIDIA software components
|
| Topic 4: Validation, Troubleshooting and Optimization | 20% | - Troubleshooting and maintenance
|
| Topic 5: GPU Resource Management | 15% | - Multi-Instance GPU (MIG) configuration
|
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NEW QUESTION # 42
When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?
Answer: D
Explanation:
The primary goal is to reduce latency and maximize bandwidth. Minimizing hops between frequently communicating GPUs is essential for achieving this. Server placement for cooling is a secondary concern, and topology choice should prioritize performance over simplicity. Subnet configuration and server placement relative to the network edge are less critical for NVLink-specific performance.
NEW QUESTION # 43
A system administrator needs to change the RAID level of DGX station and use the script included in the DGX software with no data on the array. What are the options the administrator can choose?
Answer: B
Explanation:
The DGX Station RAID configuration script supports configuring the data array as RAID 0 or RAID 5. RAID 0 provides maximum performance and capacity, while RAID 5 provides fault tolerance with usable capacity tradeoff.
NEW QUESTION # 44
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?
Answer: C
Explanation:
Uneven GPU utilization in a multi-node cluster is a classic symptom of PCIe/NUMA imbalance. In a DGX H100, each set of GPUs is physically closer to a specific CPU socket and a specific set of high-speed NICs. If the workload (HPL) is launched without strict affinity, an MPI rank running on a CPU core attached to Socket
0 might attempt to control a GPU attached to Socket 1. This forces data to cross the inter-processor links (UPI
/QPI), which have significantly higher latency and lower bandwidth than a direct PCIe path. This " bottlenecking " causes some GPUs to wait for data longer than others, leading to the uneven utilization observed. The verified solution is to use an orchestration script or flags that enforce Affinity. By setting --gpu- affinity and --cpu-affinity, the administrator ensures that each GPU is managed by a CPU core on its local NUMA node. This alignment minimizes latency and ensures that every GPU in the cluster receives data at the same rate, resulting in the flat, high-utilization profile required for a successful HPL burn-in record.
NEW QUESTION # 45
You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?
Answer: B
Explanation:
The NVIDIA Container Toolkit enables compatibility between the host driver and the CUDA version within the container (B). Using a Docker image based on will allow your application to leverage CUDA 12 features. Upgrading the host driver (A) is an option but not necessary and may introduce other compatibility issues. Setting (C) is not a standard or reliable approach. Mounting CUDA libraries from a volume (D) is complex and might not resolve driver version mismatches. Downgrading the application (E) avoids the problem but sacrifices access to CUDA 12 features. Because NVIDIA ensures a degree of backwards compatibility, the newer toolkit in the container can often work with an older driver on the host.
NEW QUESTION # 46
After running a 24-hour stress test on a DGX node, the administrator should verify which two key metrics to ensure system stability?
Answer: A
Explanation:
A 24-hour stress test (using tools like HPL or NCCL) is designed to push the thermal and electrical limits of a DGX system. To verify a " Pass, " the administrator must ensure that the hardware maintained its performance targets without degradation. Consistent GPU utilization > 95% confirms that the workload successfully saturated the compute cores for the entire duration. Crucially, the absence of thermal throttling events (verified via nvidia-smi -q -d PERFORMANCE) ensures that the system ' s cooling solution (fans and heatsinks) is adequate for the environment; if throttling occurred, the GPUs would have slowed down to protect themselves, indicating a potential cooling failure or environmental heat issue. While power consumption (Option D) and CPU usage (Option A) are interesting, they are not the primary indicators of " Stability " under extreme AI training loads. System stability is defined by the ability to run at peak speeds indefinitely without hardware-level interventions or slowdowns.
NEW QUESTION # 47
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