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NVIDIA AI Infrastructure Sample Questions (Q44-Q49):

NEW QUESTION # 44
You are designing a storage solution for a new AI inference cluster that requires extremely low latency for model serving. Which storage technology and configuration would be MOST suitable to meet this stringent latency requirement?

A. Amazon S3 object storage accessed over a high-bandwidth internet connection.
B. A traditional Fibre Channel SAN with a dedicated storage array.
C. A software-defined storage (SDS) solution running on commodity hardware with SATA SSDs.
D. NVMe-oF (NVMe over Fabrics) using RDMA over Converged Ethernet (RoCE) connected to a cluster of NVMe drives.
E. A distributed file system deployed on spinning HDDs with a large read-ahead cache.

Answer: D

Explanation:
NVMe-oF with RoCE (option B) provides the lowest latency due to the combination of NVMe's low-latency access to flash storage and RDMA's ability to bypass the CPU for data transfer. HDDs (A) are too slow. SATA SSDs (C) are slower than NVMe. Object storage (D) introduces network latency. Fibre Channel (E) is a viable option, but NVMe-oF typically offers lower latency and greater flexibility.

NEW QUESTION # 45
You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs. What could be the possible causes for this?

A. Insufficient CPU cores are available for the Triton Inference Server, limiting its ability to spawn multiple inference processes.
B. The Triton Inference Server is not configured to enable CUDA Multi-Process Service (MPS).
C. The GPUs are not of the same type and Triton cannot properly schedule across them.
D. The model configuration file does not specify the 'instance_group' parameter correctly to utilize multiple GPUs.
E. The models are not optimized for multi-GPU inference, resulting in a single GPU bottleneck.

Answer: B,D

Explanation:
The 'instance_group' parameter in the model configuration dictates how Triton distributes the model across GPUs. Without proper configuration, it may default to a single GPIJ. CUDA MPS allows multiple CUDA applications (in this case, Triton inference processes) to share a single GPU, improving utilization. Insufficient CPU cores or non-optimized models could limit performance, but wouldn't necessarily restrict usage to a single GPIJ. While dissimilar GPIJs can affect performance, Triton will attempt to schedule across them if configured correctly.

NEW QUESTION # 46
You are implementing a distributed deep learning training setup using multiple servers connected via NVLink switches. You want to ensure optimal utilization of the NVLink interconnect. Which of the following strategies would be MOST effective in achieving this goal?

A. Disable peer-to-peer GPU memory access within each server to avoid contention.
B. Configure NCCL to use GPUDirect RDMA for inter-GPU communication across servers.
C. Implement a data compression algorithm that can be processed by the CPU before sending data over NVLink.
D. Increase the batch size to reduce the frequency of inter-GPU communication.
E. Use a standard TCP/IP socket connection for inter-GPU communication across servers.

Answer: B,D

Explanation:
Configuring NCCL to use GPUDirect RDMA is key as it bypasses the CPU and reduces latency. Increasing batch size helps amortize the cost of communication. TCP/IP will not utilize NVLink; CPU-based compression adds overhead, and disabling peer-to-peer access hurts performance. GPUDirect RDMA allows direct memory access and it is the most critical piece in NVLink switch based system setup. Large batch size would also help to saturate the link bandwidth and less freqeuntly communicate between GPUs.

NEW QUESTION # 47
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. Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
B. Power throttling; Check the DPU's power consumption and thermal status via the BMC.
C. Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.
D. Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.
E. Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.

Answer: A

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 # 48
You've installed a DGX A100 server During the initial hardware validation, you observe that one of the GPUs is consistently reporting lower performance compared to the others. Which troubleshooting steps should you take, in the CORRECT order, to diagnose the problem?

A. 1. Check the power supply connections. 2. Check GPU temperature. 3. Reseat the GPIJ. 4. Run GPU diagnostics.
B. 1. Reseat the GPU. 2. Check GPU temperature. 3. Update the GPU driver. 4. Run GPU diagnostics.
C. 1. Check GPU temperature. 2. Reseat the GPU. 3. Run GPU diagnostics. 4. Update the GPU driver.
D. 1. Update the GPU driver. 2. Run GPU diagnostics. 3. Check GPU temperature. 4. Reseat the GPIJ.
E. 1. Run GPU diagnostics. 2. Reseat the GPIJ. 3. Check GPU temperature. 4. Update the GPU driver.

Answer: C

Explanation:
Checking temperature is crucial first to avoid damaging the GPU if it's overheating. Reseating addresses potential connectivity issues. Running diagnostics identifies hardware faults. Updating the driver should be done after hardware checks to ensure the card isn't faulty.

NEW QUESTION # 49
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100% Pass Quiz NVIDIA - NCP-AII Fantastic Testing Center

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NVIDIA AI Infrastructure Sample Questions (Q44-Q49):

Quiz 2025 NVIDIA High Hit-Rate NCP-AII: NVIDIA AI Infrastructure Testing Center

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