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NEW QUESTION # 86
You are leading a project to enhance the energy efficiency of a data center that heavily relies on AI workloads. NVIDIA suggests moving beyond traditional metrics like Power Usage Effectiveness (PUE) to better capture the efficiency of modern data centers. Which strategy should you prioritize?
Answer: B
Explanation:
Traditional data center metrics like PUE (Power Usage Effectiveness) only measure how much energy is
"wasted" by cooling and power delivery relative to the IT load; they say nothing about how efficiently that IT load is performing its task. In an AI Factory, "Efficiency" is better defined by the amount of AI training or inference performed per watt. NVIDIA advocates for the use of workload-specific benchmarks, such as MLPerf, to quantify this. MLPerf measures the time and energy required to complete standardized AI tasks (like training a ResNet-50 model or an LLM). By prioritizing these benchmarks (Option C), an organization can compare the energy efficiency of different hardware architectures (e.g., A100 vs. H100) or different software optimizations (e.g., FP8 vs. FP16). For example, even if an H100 system draws more peak power than an older system, its ability to complete a training job 9x faster results in a significantly lower "Total Energy Consumed per Job". This shift from "infrastructure efficiency" (PUE) to "computing efficiency" (MLPerf-per-watt) is essential for modern AI data centers aiming for sustainability and cost-effective scaling.
NEW QUESTION # 87
You are setting up network fabric ports for hosts in an NVIDIA-Certified Professional A1 Infrastructure (NCP-AII) environment. You need to configure Jumbo Frames to improve network throughput. What is the typical MTU (Maximum Transmission Unit) size you would set on the network interfaces and switches, and why?
Answer: D
Explanation:
For optimal performance in an A1 training environment, Jumbo Frames (MTU of 9000 bytes) are recommended. Increasing the MTU reduces the number of packets required to transmit a given amount of data, thereby reducing processing overhead and improving overall throughput. While technically larger MTUs are possible, 9000 is the standard for Jumbo Frames.
NEW QUESTION # 88
You are troubleshooting slow I/O performance in a deep learning training environment utilizing BeeGFS parallel file system. You suspect the metadata operations are bottlenecking the training process. How can you optimize metadata handling in BeeGFS to potentially improve performance?
Answer: E
Explanation:
Metadata operations like file creation, deletion, and attribute modification can become a bottleneck in parallel file systems. Increasing the number of metadata servers (MDSs) (option C) and distributing the metadata load across them is the direct way to improve metadata handling performance in BeeGFS.
NEW QUESTION # 89
After a firmware upgrade on a DGX H100, the administrator notices that one GPU is not detected by the system. Which troubleshooting step should be performed first to identify the root cause?
Answer: D
Explanation:
After a firmware upgrade, the first step is to check the upgrade logs and run nvsm show health to identify whether the missing GPU is related to firmware activation, hardware detection, PCIe issues, or a component fault. This provides diagnostic evidence before rerunning updates or replacing hardware.
NEW QUESTION # 90
You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?
Answer: D
Explanation:
eBPF with XDP is the most suitable technology for deep packet inspection (DPI) on BlueField. It allows you to run custom code at near-line speed to inspect packets and apply security rules based on application signatures. TC and Netfilter are less efficient for DPI, OVS/OpenFlow are more for switching policies, and IPsec focuses on encryption.
NEW QUESTION # 91
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