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NVIDIA AI Infrastructure Sample Questions (Q252-Q257):

NEW QUESTION # 252
A data center is designed for A1 training with a high degree of east-west traffic. Considering cost and performance, which network topology is generally the most suitable?

A. Three-Tier
B. Mesh
C. Ring
D. Bus
E. Spine-Leaf

Answer: E

Explanation:
Spine-Leaf architecture is designed to handle high-bandwidth, low-latency traffic patterns characteristic of AI training. It provides a non-blocking fabric with equal cost paths between any two servers, making it ideal for east-west communication. Three-Tier is more suited for traditional applications with north-south traffic. Ring and Bus are less scalable and perform poorly under heavy load. Mesh is complex and expensive for large-scale deployments.

NEW QUESTION # 253
A BlueField-3 DPUis configured to run both control plane and data plane functions. After a recent software update, you notice that the data plane performance has significantly degraded, but the control plane remains responsive. What is the MOST likely cause, assuming the update didn't introduce any code bugs, and what is the BEST approach to diagnose this issue?

A. Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.
B. Power throttling; Check the DPU's power consumption and thermal status via the BMC.
C. Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
D. Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.
E. Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.

Answer: C

Explanation:
Resource contention is the MOST likely cause, assuming no code bugs. The update may have increased the resource demands of either the control or data plane, leading to contention. Profiling the data plane processes with 'perf or 'bpftrace' helps pinpoint the bottlenecks. Downgrading drivers or reflashing firmware are more drastic steps to take after confirming resource contention isn't the issue.

NEW QUESTION # 254
You encounter a situation where a container running with GPU support is experiencing significant performance degradation compared to running the same application directly on the host. You have already verified that the NVIDIA drivers are correctly installed and the NVIDIA Container Toolkit is properly configured. Which of the following could be contributing factors to this performance difference?
(Select all that apply)

A. The '-ipc=host' flag is not used when running the container, causing inter-process communication overhead.
B. Insufficient bandwidth between CPU and GPU
C. CPU pinning or NIJMA affinity is not properly configured for the container, leading to inefficient memory access.
D. The kernel version within the container is significantly different from the host kernel, leading to driver compatibility issues.
E. The container is using a significantly older version of the CUDA runtime compared to the host.

Answer: C,E

Explanation:
Using an older CUDA runtime within the container (A) can lead to performance degradation due to missing optimizations or compatibility issues with the application. Improper CPU pinning and NUMA affinity (B) can cause the container to access memory inefficiently, especially in multi-socket systems. '--ipc=host' (C) can improve performance in some cases by sharing the host's IPC namespace, but it's not always necessary and can have security implications. Kernel version differences (D) are generally handled by the NVIDIA Container Toolkit, which ensures compatibility. Insufficient bandwidth between CPU and GPU (E) might be caused by hardware issue.

NEW QUESTION # 255
You have installed NVIDIA drivers using the .run' installer on a system running Ubuntu. However, after each kernel update, the NVIDIA drivers stop working. What is the most effective way to address this issue permanently?

A. Re-run the '.run' installer after each kernel update.
B. Create a script that symlinks the old kernel modules to the new kernel directory.
C. Disable automatic kernel updates.
D. Manually compile the NVIDIA kernel modules after each kernel update.
E. Use DKMS (Dynamic Kernel Module Support) to automatically rebuild the NVIDIA kernel modules after each kernel update.

Answer: E

Explanation:
DKMS (Dynamic Kernel Module Support) is the most effective solution. It automatically rebuilds kernel modules (like NVIDIA's) whenever a new kernel is installed. Re-running the Irun' installer is a manual and error-prone process. Disabling kernel updates is not recommended for security reasons. Manually compiling is complex and time-consuming. Symlinking old modules is unlikely to work due to kernel API changes.

NEW QUESTION # 256
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?

A. Disable other models running on the same GPU to free up memory.
B. Reduce the batch size and concurrency of the offending model in the Triton configuration.
C. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.
D. Upgrade the GPUs to models with larger memory capacity.
E. Increase the system RAM to accommodate the growing memory footprint.

Answer: B,C

Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.

NEW QUESTION # 257
......

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Quiz 2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure–Professional Valid Braindumps Sheet

Quiz 2025 NCP-AII: Trustable Valid Braindumps NVIDIA AI Infrastructure Sheet

NVIDIA AI Infrastructure Sample Questions (Q252-Q257):

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