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[General] Top NCP-AII Valid Exam Camp Pdf Free PDF | High-quality Practice Test NCP-AII Fe

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【General】 Top NCP-AII Valid Exam Camp Pdf Free PDF | High-quality Practice Test NCP-AII Fe

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NVIDIA AI Infrastructure Sample Questions (Q158-Q163):NEW QUESTION # 158
You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself. Which of the following methods would be the most effective in diagnosing the physical NVLink link health?
  • A. Using 'ping' to check network connectivity between the servers.
  • B. Physically inspecting the NVLink cables for damage.
  • C. Running a CUDA-aware memory bandwidth test specifically designed for NVLink.
  • D. Examining system logs for NVLink-related error messages.
  • E. Using 'iperf3' to measure network throughput between the servers.
Answer: B,C,D
Explanation:
A CUDA-aware memory bandwidth test can specifically measure the NVLink link's performance. System logs can reveal hardware- level errors. Physical inspection can identify damaged cables. 'iperf3' and 'pings are network-level tools and don't directly test the NVLink link. Checking for error messages in System Logs also helps identify potential issues related to the NVLink switch and the link connections.

NEW QUESTION # 159
You are configuring a network for a distributed training job using multiple DGX servers connected via InfiniBand. After launching the training job, you observe that the inter-GPU communication is significantly slower than expected, even though 'ibstat' shows all links are up and active. What is the MOST likely cause of this performance bottleneck?
  • A. The CPU frequency scaling governor is set to 'powersave', limiting CPU performance.
  • B. Incorrect placement of GPUs across NUMA nodes, leading to increased inter-node latency.
  • C. The default MTU size of 1500 is too small for efficient large data transfers.
  • D. The InfiniBand subnet manager (SM) is configured incorrectly or experiencing performance issues (e.g., path selection is suboptimal, congestion control is not enabled).
  • E. The RDMA memory registration limit is too low, causing frequent memory registration and unregistration overhead.
Answer: D
Explanation:
While the other options could contribute to performance issues, the subnet manager (SM) is the MOST likely culprit. A poorly configured or malfunctioning SM can lead to suboptimal path selection (e.g., routing traffic through congested links or longer paths), which significantly increases latency and reduces bandwidth. Congestion control mechanisms, if not properly configured, can also fail to mitigate congestion, leading to packet loss and retransmissions, further degrading performance. Checking the SM logs and configuration is the first step in diagnosing this issue. Incorrect NIJMA placement, small MTIJ, powersave governor, and memory registration limits could also impact performance but are less likely to be the primary bottleneck if the links are up.

NEW QUESTION # 160
A GPU in your AI server consistently overheats during inference workloads. You've ruled out inadequate cooling and software bugs.
Running 'nvidia-smi' shows high power draw even when idle. Which of the following hardware issues are the most likely causes?
  • A. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.
  • B. Degraded thermal paste between the GPU die and the heatsink.
  • C. Insufficient system RAM.
  • D. A BIOS setting that is overvolting the GPU.
  • E. Incorrectly seated GPU in the PCle slot, leading to poor power delivery.
Answer: A,B,E
Explanation:
Degraded thermal paste loses its ability to conduct heat effectively. A failing VRM can cause excessive power draw and heat generation. An incorrectly seated GPU can cause instability and poor power delivery, leading to overheating. Overvolting in BIOS will definitely cause overheating. While insufficient RAM can cause performance issues, it is less likely to lead to overheating.

NEW QUESTION # 161
You are tasked with optimizing an Intel Xeon scalable processor-based server running a TensorFlow model with multiple NVIDIA GPUs.
You observe that the CPU utilization is low, but the GPU utilization is also not optimal. The profiler shows significant time spent in 'tf.data' operations. Which of the following actions would MOST likely improve performance?
  • A. Upgrade the server's network adapter to a faster interface, such as 100Gb
  • B. Reduce the global batch size to improve memory utilization.
  • C. Increase the number of threads used for CPU-bound operations in TensorFlow using 'tf.config.threading.set_intra_op_parallelism_threads()'.
  • D. Use 'tf.data.AUTOTIJNE to allow TensorFlow to dynamically optimize the data pipeline.
  • E. Enable XLA (Accelerated Linear Algebra) compilation in TensorFlow.
Answer: D
Explanation:
'tf.data' performance issues often stem from inefficient data pipelines. 'tf.data.AIJTOTUNE allows TensorFlow to dynamically optimize the pipeline by adjusting parameters such as prefetch buffer size and the number of parallel calls to transformation functions. XLA compilation optimizes graph execution, but 'tf.data' issues need to be addressed first. Increasing CPU threads might help but 'AUTOTUNE is more specific to the problem. A smaller batch size could negatively impact GPU utilization. Network upgrades are irrelevant as the problem lies within the server.

NEW QUESTION # 162
Which of the following are key benefits of using NVIDIA Spectrum-X switches in an A1 infrastructure compared to traditional Ethernet switches? (Select THREE)
  • A. Lower cost per port.
  • B. Support for RoCE (RDMA over Converged Ethernet) and InfiniBand protocols, enabling high-bandwidth, low-latency communication.
  • C. Advanced telemetry and monitoring capabilities for network performance optimization.
  • D. Hardware-based acceleration for collective communication operations used in distributed A1 training.
  • E. Native support for IPv6.
Answer: B,C,D
Explanation:
Spectrum-X switches are designed for high-performance computing and A1 workloads. They support RoCE and InfiniBand for low- latency communication, offer advanced telemetry for network optimization, and include hardware-based acceleration for collective communication operations, improving the efficiency of distributed A1 training. While Spectrum-X supports IPv6, this is also a common feature in modern Ethernet switches. Spectrum-X switches typically have a higher cost per port compared to basic Ethernet switches due to their advanced features and performance.

NEW QUESTION # 163
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