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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GPU Resource Management | 15% | - Multi-Instance GPU (MIG) configuration
|
| Topic 2: Networking and Storage Configuration | 20% | - NVIDIA networking solutions
|
| Topic 3: Software Stack Deployment | 25% | - NVIDIA software components
|
| Topic 4: System and Server Bring-up | 20% | - Hardware installation and validation
|
| Topic 5: Validation, Troubleshooting and Optimization | 20% | - Cluster validation and benchmarking
|
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問題 #43
After Spectrum-X fabric deployment, NCCL tests show intermittent latency spikes. Which network condition most severely impacts East-West bandwidth?
答案：B
解題說明：
Packet loss is especially damaging to East-West AI fabric performance because NCCL collective operations depend on reliable, high-throughput GPU-to-GPU communication. Even small packet- loss rates can trigger retries and pipeline stalls, causing latency spikes and reducing effective bandwidth across the fabric.
問題 #44
You are training a deep neural network using NCCL to coordinate communication across four GPUs in a single node. During early performance testing, you notice inconsistent scaling and longer-than-expected training times, even though all GPUs are being used. Which strategy would most effectively improve NCCL efficiency and collective operation performance in this setting?
答案：B
解題說明：
NCCL collective performance depends on balanced work across GPUs so that no GPU becomes a straggler during synchronization. Equalizing the batch portion per GPU keeps computation and communication aligned, improving scaling efficiency and reducing delays in collective operations.
問題 #45
An engineer needs to verify NVLink isolation on a single node with 8 GPUs. Which NCCL test configuration stresses switch bisection bandwidth?
答案：B
解題說明：
To validate the robustness of theNVLink Switch fabricin a DGX H100, engineers must test how the switches handle traffic when the cluster is logically partitioned. While a standard all_reduce_perf test (Option D) shows aggregate throughput, it may not reveal issues with specific internal switch paths. Using the NCCL_TESTS_SPLIT environment variable allows for more granular stress testing. Specifically, using a bitwise mask like "AND 0x1" (Option B) creates specific traffic subsets that force data through the internal NVLink switch bisection. This ensures that even when only half the GPUs are communicating-or when specific patterns are used-the switches can maintain full wire speed without internal contention. This is a critical validation step during the "Bring-up" phase to ensure there are no manufacturing defects in the NVSwitch baseboard or the high-speed traces connecting the GPU modules.
問題 #46
You are configuring a RoCEv2 (RDMA over Converged Ethernet) network using BlueField-2 DPUs. You are observing packet loss and performance degradation. You suspect that Congestion Control is not working correctly. What configuration parameter most directly impacts RoCEv2 congestion control behavior?
答案：B
解題說明：
ECN is the key mechanism for RoCEv2 congestion control. It allows network devices to signal congestion to the endpoints, which can then reduce their transmission rate. Proper ECN configuration on both the switches and the DPIJ interfaces is essential for effective congestion control. While PFC can prevent packet loss due to buffer overflow, it doesn't address congestion in the same way as ECN. The other options are less directly related to RoCEv2 congestion control.
問題 #47
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?
答案：D
解題說明：
Deep learning training involves iterating over a dataset many times (epochs). If a 32-node cluster pulls the same dataset from a central NFS storage server for every epoch, the network and storage fabric quickly become a bottleneck due to " Incast " traffic. By using the high-speed NVMe drives internal to a DGX system (configured in RAID-0 for maximum performance, not redundancy), the system can implement a local cache.
During the first epoch, data is pulled from the remote storage and simultaneously written to the local SSDs.
For all subsequent epochs, the training framework reads the data directly from the local RAID-0 array. This significantly reduces NFS traffic and network congestion, allowing the training to proceed at the full speed of the local NVMe storage ($25\text{ GB/s}+$ on modern DGX systems). Option C is incorrect because RAID-
0 provides no redundancy; if a drive fails, the cache is lost, but since it is just a cache, the data still exists on the primary storage. Option B refers to GPUDirect Storage, which is a separate technology from local RAID-
0 caching.
問題 #48
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