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NVIDIA AI Infrastructure Sample Questions (Q196-Q201):

NEW QUESTION # 196
You are troubleshooting performance issues in an A1 training clusten You suspect network congestion. Which of the following network monitoring tools would be MOST helpful in identifying the source of the congestion?

A. iPerf/Netperf
B. Traceroute
C. tcpdump/Wireshark
D. netstat
E. Ping

Answer: A,C

Explanation:
iPerf/Netperf can measure network bandwidth and latency between nodes, allowing you to quantify congestion. tcpdump/Wireshark can capture and analyze network packets, providing detailed insights into traffic patterns and potential bottlenecks. Ping and Traceroute are useful for basic connectivity testing but not for detailed congestion analysis. netstat shows network statistics but doesn't capture packet-level details.

NEW QUESTION # 197
You are configuring a network bridge on a Linux host that will connect multiple physical network interfaces to a virtual machine. You need to ensure that the virtual machine receives an IP address via DHCP. Which of the following is the correct command sequence to create the bridge interface 'br0', add physical interfaces 'eth0' and 'eth1' to it, and bring up the bridge interface? Assume the required packages are installed. Consider using 'ip' command.

Answer: B

Explanation:
Option D is the correct sequence using the Sip' command. First, create the bridge ' bro'. Then, add the physical interfaces 'eth0 and "eth1' as slaves to the bridge. Next, bring up the physical interfaces. After that, bring up the bridge interface . Finally, use "dhclient bro to obtain an IP address for the bridge via DHCP. Option C is the old way, using 'brctr and 'ifconfig', which are deprecated. The others lack the crucial step of bringing up the bridge after attaching the physical interfaces and before running 'dhclient'.

NEW QUESTION # 198
An A1 server is exhibiting unusually high CPU utilization during a GPU-accelerated workload. How can you determine if the CPU is becoming a bottleneck, preventing the GPUs from achieving their full potential?

A. All of the above
B. Profile the application with a CPU profiler (e.g., 'perf) to identify CPU-bound functions.
C. Monitor CPU utilization using 'top' or 'htop' while the workload is running; sustained high utilization suggests a bottleneck.
D. Use 'nvidia-smi' to check the GPU utilization; low GPIJ utilization combined with high CPU utilization indicates a potential CPU bottleneck.
E. Run a CPU-intensive benchmark in parallel with the GPIJ workload to observe performance degradation.

Answer: A

Explanation:
All the options provide valid methods for identifying a CPU bottleneck. Monitoring CPU and GPU utilization, profiling the application, and running parallel benchmarks all help to determine if the CPU is limiting GPU performance.

NEW QUESTION # 199
You have a large dataset stored on a network file system (NFS) and are training a deep learning model on an AMD EPYC server with NVIDIA GPUs. Data loading is very slow. What steps can you take to improve the data loading performance in this scenario? Select all that apply.

A. Reduce the batch size to decrease the amount of data loaded per iteration.
B. Increase the number of NFS client threads on the AMD EPYC server.
C. Switch to a parallel file system like Lustre or BeeGFS.
D. Mount the NFS share with the 'nolock' option.
E. Use a local SSD or NVMe drive to cache frequently accessed data.

Answer: B,C,E

Explanation:
Increasing NFS client threads enables more concurrent data access. Caching frequently accessed data on a local SSD/NVMe drive reduces network I/O. Switching to a parallel file system provides higher bandwidth and lower latency compared to NFS. 'nolock' can improve performance but sacrifices data consistency. Reducing batch size reduces the amount of data loaded but doesn't address the underlying NFS bottleneck.

NEW QUESTION # 200
You are troubleshooting a network performance issue in your NVIDIA Spectrum-X based A1 cluster. You suspect that the Equal-Cost Multi-Path (ECMP) hashing algorithm is not distributing traffic evenly across available paths, leading to congestion on some links. Which of the following methods would be MOST effective for verifying and addressing this issue?

A. Restart the switches to force the ECMP hashing algorithm to recalculate paths.
B. Use switch telemetry tools (e.g., NVIDIA What's Up Gold, Mellanox NEO, or similar) to monitor link utilization across all available paths between the nodes. Look for significant imbalances in traffic volume.
C. Reduce the TCP window size.
D. Use 'ping' or 'traceroute' to analyze the paths taken by packets between the affected nodes. If they always take the same path, ECMP is likely not working correctly.
E. Disable ECMP entirely and rely solely on static routing.

Answer: B

Explanation:
Switch telemetry tools provide the most direct and comprehensive way to monitor link utilization and identify imbalances in traffic distribution caused by ECMP. While 'ping' and 'traceroute' can provide path information, they don't give insight into traffic volume. Restarting the switches might temporarily alleviate the issue but doesn't address the underlying problem with the ECMP hashing. Disabling ECMP is a last resort and can reduce overall bandwidth.

NEW QUESTION # 201
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100% Pass Quiz 2025 NVIDIA NCP-AII: High Hit-Rate NVIDIA AI Infrastructure PDF Dumps Files

2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure –Reliable PDF Dumps Files

NVIDIA AI Infrastructure Sample Questions (Q196-Q201):

Quiz NVIDIA - NCP-AII - NVIDIA AI Infrastructure Newest PDF Dumps Files

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