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NCP-AII latest valid questions & NCP-AII vce pdf dumps & NCP-AII study prep material

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NVIDIA AI Infrastructure Sample Questions (Q110-Q115):

NEW QUESTION # 110
You are tasked with validating a newly installed NVIDIAAIOO Tensor Core GPU within a server. You need to confirm the GPU is correctly recognized and functioning at its expected performance level. Describe the process, including commands and tools, to verify the following aspects: 1) GPU presence and basic information, 2) PCle bandwidth and link speed, and 3) Sustained computational performance under load.

A. 1) Check BIOS settings for GPU detection. 2) Use 'Ispci -vv' to check PCle speed. 3) Run a PyTorch ImageNet training script.
B. 1) Use 'nvidia-smi' for presence and basic info. 2) PCle speed is irrelevant. 3) Run the 'nvprof profiler during a CUDA application.
C. 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a CUDA-based matrix multiplication benchmark (e.g., using cuBLAS) with increasing matrix sizes and monitor performance.
D. 1) Use 'Ispci I grep NVIDIA' for presence, 'nvidia-smi' for basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a TensorFlow ResNet50 benchmark.
E. 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvlink-monitor' for bandwidth/speed. 3) Run a CPU-bound benchmark to avoid GPU bottlenecks.

Answer: C

Explanation:
'nvidia-smi' is the primary tool for NVIDIA GPU information. 'nvidia-smi -q -d pcie' provides PCle details. A CUDA-based benchmark isolates GPU performance. Other options have elements of truth but aren't complete or optimally targeted (e.g., ResNet50 relies on other frameworks). Using a CPU-bound benchmark wouldn't test the GPU's capabilities.

NEW QUESTION # 111
You are managing an A1 infrastructure based on NVIDIA Spectrum-X switches. A new application requires strict Quality of Service (QOS) guarantees for its traffic. Specifically, you need to ensure that this application's traffic receives preferential treatment and minimal latency.
What combination of Spectrum-X features and configurations would be MOST effective in achieving this?

A. Increase the MTIJ size on all interfaces to reduce packet fragmentation and overall latency.
B. Configure DiffServ Code Point (DSCP) marking on the application's traffic, map these DSCP values to specific traffic classes within the Spectrum-X switch, and configure Weighted Fair Queueing (WFQ) or Strict Priority Queueing on the egress ports.
C. Enable broadcast storm protection.
D. Use VLAN tagging to isolate the application's traffic into a separate virtual network.
E. Disable Adaptive Routing (AR) to ensure that traffic always takes the shortest path.

Answer: B

Explanation:
DSCP marking, traffic class mapping, and WFQ/Strict Priority Queueing are fundamental QOS mechanisms. DSCP marking allows you to classify traffic based on application requirements. Traffic classes within the switch provide different levels of service. WFQ and Strict Priority Queueing ensure that high-priority traffic receives preferential treatment on egress ports. The other options are less relevant to QOS guarantees for a specific application.

NEW QUESTION # 112
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?

A. The NGC service is down. Check the NVIDIA NGC status page for any known outages.
B. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'
C. The host does not have network access to NGC.
D. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.
E. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.

Answer: C,D,E

Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.

NEW QUESTION # 113
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 standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
B. Configuring the HPA based on memory utilization.
C. Using the Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.
D. 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.
E. Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.

Answer: D

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 # 114
You've installed a new NVIDIA GPU in your A1 server. After the installation and driver setup, you notice that while 'nvidia-smi' recognizes the GPU, the available memory reported is significantly lower than the GPU's specifications. What are the potential root causes and how would you systematically troubleshoot this?

A. The GPU is faulty and needs to be replaced.
B. The integrated graphics is using a significant amount of system memory, reducing what's available to the GPU. Disable the integrated graphics in the BIOS.
C. The system BIOS is incorrectly configured, limiting GPU memory allocation.
D. The reported memory is the currently allocated memory, not the total available. Run a CUDA program to allocate more memory and observe the change.
E. The driver is not correctly installed. Reinstall the latest NVIDIA driver.

Answer: B

Explanation:
Integrated graphics stealing system memory is a common cause, and disabling it frees up resources for the dedicated GPU. While a faulty GPU, BIOS settings, or driver issues are possibilities, integrated graphics is a more likely and easily verifiable cause. The reported memory is the total usable, not just allocated.

NEW QUESTION # 115
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