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NCP-AII Passleader Review - Reliable NCP-AII Exam Answers

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NVIDIA AI Infrastructure Sample Questions (Q155-Q160):

NEW QUESTION # 155
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 SLI (Scalable Link Interface)
B. NVIDIA CUDA MPS (Multi-Process service)
C. NVIDIA vGPU (virtual GPU)
D. NVIDIA NVLink
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 # 156
After installing a new NVIDIA GPU, you attempt to run a CUDA application, but you encounter the following error: 'CUDA error: CUDA driver version is insufficient for CUDA runtime version'. You have verified the driver and CUDA toolkit are installed. What is the MOST likely reason for this error, and how do you resolve it?

A. The CUDA runtime libraries are missing from the system path. Add them to the PATH variable.
B. The CUDA VISIBLE DEVICES environment variable is not set correctly.
C. The GPU is not compatible with the CUDA toolkit. Install a different GPIJ.
D. The NVIDIA driver is too old for the CUDA toolkit. Update the NVIDIA driver to a version that supports the CUDA toolkit.
E. The CUDA toolkit is too old. Update the CIJDA toolkit.

Answer: D

Explanation:
This error indicates an incompatibility between the driver and the CUDA toolkit. The most common reason is an outdated driver. The driver must be at least as new as the CUDA toolkit's minimum required driver version. CUDA VISIBLE DEVICES relates to GPU selection, not driver version.

NEW QUESTION # 157
An AI cluster needs to transmit data at 200Gbps over a distance of 2km using single-mode fiben Considering cost and performance, which transceiver type is the most appropriate?

A. 200GBASE-CR4
B. 200GBASE-SR4
C. 200GBASE-ER4
D. 200GBASE-DR4
E. 200GBASE-LR4

Answer: E

Explanation:
200GBASE-LR4 is the most appropriate. 'LR' designates Long Reach, typically up to 10km on single-mode fiber. SR4 is short reach (typically copper or very short fiber). ER4 is Extended Reach (up to 40km). CR4 is copper, for very short distances. DR4 is typically used for up to 500m to 2km distances depending on the specific implementation, so while possible LR4 is a better fit for guaranteed 2km.

NEW QUESTION # 158
An AI server utilizes a QSFP28 transceiver with MPO connector. During troubleshooting, you suspect a faulty transceiver. Which steps are most important to perform when physically inspecting and testing the transceiver?

A. Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and perform an Optical Time Domain Reflectometer (OTDR) test.
B. Verify the transceiver's DOM (Digital Optical Monitoring) information, clean the connector end-face with a dry cloth, and perform a ping test.
C. Check the transceiver's firmware version, measure the transceiver's temperature using an infrared thermometer, and perform a bit error rate test (BERT).
D. Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and verify the transceiver's DOM (Digital Optical Monitoring) information, particularly TX power, RX power, and temperature.
E. Examine the transceiver's EEPROM data, measure the supply voltage with a multimeter, and perform a link speed auto-negotiation test.

Answer: D

Explanation:
The MOST important steps involve visual inspection for damage, cleaning the connector to ensure a good optical connection, and verifying DOM information to assess the transceivers health and signal levels. OTDR is more suited for cable diagnosis. While firmware and voltage are relevant, the DOM provides immediate health indicators.

NEW QUESTION # 159
You are managing a Kubernetes cluster with NVIDIA GPUs and want to automatically scale your A1 inference deployments based on GPU utilization. Which of the following tools and configurations would you use to implement Horizontal Pod Autoscaling (HPA) based on GPU metrics?

A. Using the NVIDIA Data Center GPU Manager (DCGM) exporter to expose GPU metrics to Prometheus, and configuring the HPA to scale based on these metrics.
B. Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.
C. Using the standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
D. Using the Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.
E. Configuring the HPA based on memory utilization.

Answer: A

Explanation:
The NVIDIA DCGM exporter provides a way to expose GPU metrics to Prometheus, which can then be used by the Kubernetes HPA to automatically scale deployments based on actual GPU utilization. The standard Kubernetes HPA does not natively support GPU metrics. Other options are either inaccurate or manual.

NEW QUESTION # 160
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