NVIDIA NCP-AII試験過去問、NCP-AII模擬対策問題

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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Monitoring and Management	15-20%	- Troubleshooting basics - Performance monitoring - NVIDIA management tools - Resource utilization
Security and Best Practices	10-15%	- Security fundamentals - Compliance considerations - Operational best practices
NVIDIA AI Infrastructure Components	25-30%	- NVIDIA DGX systems - NVIDIA AI Enterprise software - Storage solutions for AI workloads - NVIDIA networking solutions (Mellanox)
Deployment and Configuration	25-30%	- Software deployment - Cluster configuration - Network configuration - System installation and setup
AI Infrastructure Fundamentals	15-20%	- Data center infrastructure requirements - GPU architecture basics - NVIDIA software stack overview - AI and Deep Learning concepts

>> NVIDIA NCP-AII試験過去問 <<

試験の準備方法-100％合格率のNCP-AII試験過去問試験-素敵なNCP-AII模擬対策問題

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NVIDIA AI Infrastructure 認定 NCP-AII 試験問題 (Q39-Q44):

質問 # 39
You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

A. Ensure that all matrix multiplications are performed using FP16 precision.
B. Enable CUDA graph capture to reduce kernel launch overhead.
C. Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
D. Decrease the learning rate to improve training stability and reduce the need for gradient clipping.
E. Increase the batch size to fully utilize the available GPU memory.

正解：C

解説：
Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.

質問 # 40
Which statement best explains why maintaining high cable signal quality is essential in modern high-speed data centers?

A. High cable signal quality enables effective use of forward error correction (FEC), which is required for reliable operation at high data rates such as 200GbE and above.
B. High cable signal quality reduces electromagnetic interference (EMI) and cross-talk, helping prevent unexpected packet drops during sustained workloads.
C. High cable signal quality ensures that cable length and connector type do not play as big a role in deploying new infrastructure in the data center.
D. High cable signal quality minimizes bit error rates and supports reliable, high-throughput communication, reducing retransmissions and congestion across the network.

正解：D

解説：
High cable signal quality keeps bit error rates low, which is critical for reliable high-speed Ethernet or InfiniBand communication. Low BER reduces retransmissions, congestion, latency spikes, and workload stalls, helping maintain stable high-throughput performance during sustained AI cluster traffic.

質問 # 41
You encounter an error during the BlueField OS flashing process using 'bfboot: 'ERROR: Could not detect a BlueField device'. Which of the following steps is MOST likely to resolve the issue?

A. Ensure the BlueField device is powered on and properly connected to the host system via PCIe.
B. Verify that the correct PCIe slot is being used. Some systems may have specific slots designated for SmartNICs.
C. Check the systems BIOS/UEFI to confirm that SR-IOV is enabled.
D. Update the 'bfboot' utility to the latest version. Older versions may have compatibility issues.
E. Install the Mellanox OFED drivers on the host system. These drivers are required for 'bfboot' to function correctly.

正解：A

解説：
The most basic and common cause of this error is a physical connection issue. Ensuring the device is powered and connected is the first step. While other options might be relevant in specific scenarios, connectivity is the most probable cause.

質問 # 42
A system administrator receives an alert about a potential hardware fault on an NVIDIA DGX A100. The GPU performance seems degraded, and the system fans are operating loudly. What step should be recommended to identify and troubleshoot the hardware fault?

A. Power drain then restart the DGX and check if the performance degradation resolves.
B. Check the NVIDIA System Management Interface (nvidia-smi) for GPU status and temperatures.
C. Run a deep learning workload to stress test the GPUs and check whether the issue persists.
D. Increase the fan speed to maximum and check whether the performance improves.

正解：B

解説：
nvidia-smi is the appropriate first diagnostic tool for checking GPU health, utilization, power, temperature, throttling, and error indicators. Since degraded performance and loud fans may point to thermal or GPU hardware issues, reviewing GPU status and temperatures helps identify the fault condition before taking corrective action.

質問 # 43
An administrator notices that a server is not collecting telemetry data such as traffic flows, performance faults, and events. In which network does this information flow?

A. Storage
B. InfiniBand management
C. Compute
D. In-band management

正解：B

解説：
Telemetry such as fabric traffic flows, performance counters, faults, and events is collected through the InfiniBand management path. If this data is not being collected, the InfiniBand management network should be checked for connectivity, configuration, and service issues.

質問 # 44
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