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Quiz NVIDIA - NCP-AII–Professional Latest Guide Files

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NVIDIA AI Infrastructure Sample Questions (Q225-Q230):

NEW QUESTION # 225
You're deploying a new cluster with multiple NVIDIAAIOO GPUs per node. You want to ensure optimal inter-GPU communication performance using NVLink. Which of the following configurations are critical for achieving maximum NVLink bandwidth?

A. All GPUs within a node must be the same model and have identical firmware versions.
B. The motherboard must support PCle Gen5 to maximize NVLink bandwidth.
C. The server must use a specific CPU model to leverage NVLink capabilities.
D. The NVIDIA driver must be configured to enable NVLink; it is disabled by default.
E. GPUs should be physically installed in slots that maximize direct NVLink connections based on the server's architecture.

Answer: A,E

Explanation:
For optimal NVLink performance, several conditions must be met. GPUs of the same model and firmware ensure compatibility and prevent performance bottlenecks. Physical placement is critical; GPUs must be installed in slots that maximize direct NVLink connections, as defined by the server's architecture and documentation. While PCle Gen5 is beneficial for overall system performance, it does not directly impact NVLink bandwidth. NVLink is typically enabled by default. Some CPU models may be preferable, but it's the motherboard's NVLink topology that is more important.

NEW QUESTION # 226
You are setting up a new AI inference server in a colocation facility. The server will be connected to a 100GbE switch managed by the facility. You have the option to use either a single-mode fiber connection with an LR4 transceiver or a multi-mode fiber connection with an SR4 transceiver. The distance between your server and the switch is approximately 75 meters. Considering cost, signal quality, and future scalability, which option is MOST suitable?

A. AOC cable, as it provides better future scalabilty than any other type of connection
B. LR4 transceiver with single-mode fiber, as it provides better signal quality over distance.
C. LR4 transceiver with single-mode fiber, as it provides better power efficiency.
D. Either option is equally suitable; the choice is arbitrary.
E. SR4 transceiver with multi-mode fiber, as it is typically more cost-effective for shorter distances.

Answer: E

Explanation:
For a distance of 75 meters, an SR4 transceiver with multi-mode fiber is the most suitable option. It is typically more cost-effective than LR4 transceivers and provides adequate signal quality for this distance. LR4 transceivers are designed for longer distances and are more expensive. While AOCs are convenient, SR4 with multimode fiber is more cost effective. Cost-effectiveness is the defining factor between them.

NEW QUESTION # 227
You have a server with 8 NVIDIA A100 GPUs. You want to configure each GPU to be used by a different user, ensuring resource isolation and preventing one user's workload from monopolizing the entire GPU. Which NVIDIA technology is most suitable for this scenario?

A. NVIDIA vGPU (virtual GPU)
B. NVIDIA NVLink
C. NVIDIA SLI (Scalable Link Interface)
D. NVIDIA CUDA MPS (Multi-Process service)
E. NVIDIA MIG (Mufti-instance GPU)

Answer: E

Explanation:
NVIDIA MIG (Multi-lnstance GPU) is designed specifically for this scenario. It allows partitioning a single physical GPU into multiple isolated GPU instances, each with its own dedicated memory, compute, and isolation. CUDA MPS allows multiple CUDA applications to share a single GPU but does not provide the same level of resource isolation as MIG. vGPU is primarily for virtualized environments. SLI and NVLink are for GPU interconnection, not resource isolation.

NEW QUESTION # 228
You suspect a power supply issue is causing intermittent GPU failures in a server with four NVIDIAAIOO GPUs. The server is rated for a peak power consumption of 3000W. You have a power meter available. Which of the following methods provides the most accurate assessment of the server's power consumption under full GPU load?

A. Use the power meter to measure the server's power consumption at idle and multiply by four.
B. Run 'nvidia-smi' and sum the reported power consumption for each GPIJ.
C. Check the server's BIOS for power consumption readings.
D. Add the maximum power rating of each GPU to the CPU's TDP (Thermal Design Power).
E. Use the power meter to measure the server's power consumption while running a synthetic benchmark that fully utilizes all GPIJs simultaneously.

Answer: E

Explanation:
Explanation:Measuring power consumption with a power meter while running a synthetic benchmark provides the most accurate assessment. 'nvidia-smi' reports GPU power consumption, but it doesn't account for the power draw of other components (CPU, memory, etc.). Idle power measurements are irrelevant for assessing peak load. BIOS readings can be unreliable. Simply adding up component power ratings doesn't account for inefficiencies or dynamic power management.

NEW QUESTION # 229
You're optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using 'nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence. What are possible causes and solutions?

A. The CUDA code contains conditional branches that lead to different execution paths for different threads within the same warp. Rewrite the CUDA code to minimize branching and favor uniform execution paths within warps.
B. The GPUs are overheating, causing thermal throttling. Improve the server's cooling.
C. The CUDA compiler is generating suboptimal code. Try using different compiler optimization flags (e.g., '-O3' or '-ftz=true').
D. The input data is not properly aligned in memory. Ensure that data is aligned to 128-byte boundaries using aligned memory allocation techniques.
E. The CUDA driver version is incompatible with the CUDA toolkit version. Update the CUDA driver to a compatible version.

Answer: A,D

Explanation:
Thread divergence occurs when threads within the same warp take different execution paths due to conditional branches, leading to serialization. Re-writing code to minimize branching is critical. Memory alignment ensures that threads access memory efficiently and doesn't cause thread divergence stalls, which is often misaligned. Thermal throttling is a possible cause of fluctuating utilization but doesn't directly explain the stalls identified in the profiler. Compiler flags and driver version issues can cause performance problems but are less likely to cause frequent thread divergence stalls.

NEW QUESTION # 230
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