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NVIDIA AI Infrastructure Sample Questions (Q237-Q242):

NEW QUESTION # 237
A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames). What is the MOST likely consequence of this MTU mismatch, and what action should you take?

A. The server will be unable to communicate with the NAS device. Reduce the MTU size on the server to match the MTU size of the NAS device.
B. Data packets will be fragmented, leading to increased overhead and reduced performance. Configure both the server and the NAS device to use the same MTU size (either 1500 or 9000).
C. Data packets will be retransmitted, increasing the latency but still getting the full throughput. Configure the server to use Path MTU Discovery (PMTUD).
D. The connection between the server and the NAS device will be unreliable, resulting in data corruption. Increase the MTU size on both devices to the maximum supported value.
E. The data transfer will be limited to the lowest common MTU size, but there will be no significant performance impact. No action is required.

Answer: B

Explanation:
An MTU mismatch (option A) will cause fragmentation, where larger packets are broken down into smaller packets before being transmitted, adding overhead and reducing performance. The solution is to configure both devices to use the same MTU size. Choosing 1500 ensures compatibility, while 9000 requires the entire network path to support jumbo frames.

NEW QUESTION # 238
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. Update the GPU driver. 2. Run GPU diagnostics. 3. Check GPU temperature. 4. Reseat the GPIJ.
B. 1. Check GPU temperature. 2. Reseat the GPU. 3. Run GPU diagnostics. 4. Update the GPU driver.
C. 1. Check the power supply connections. 2. Check GPU temperature. 3. Reseat the GPIJ. 4. Run GPU diagnostics.
D. 1. Reseat the GPU. 2. Check GPU temperature. 3. Update the GPU driver. 4. Run GPU diagnostics.
E. 1. Run GPU diagnostics. 2. Reseat the GPIJ. 3. Check GPU temperature. 4. Update the GPU driver.

Answer: B

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 # 239
You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?

A. Manually copy the necessary CUDA libraries from the host into the container using 'docker cp' before running the application.
B. Install the required CUDA toolkit version directly on the host operating system. The NVIDIA Container Toolkit will automatically map it into the container.
C. Use a base image (e.g., from NVIDIA NGC) that already includes the desired CUDA toolkit version. This approach provides a consistent and reproducible environment.
D. Specify the desired CUDA version when running the container using the '-env flag. The NVIDIA Container Toolkit will dynamically install the CUDA version during container startup.
E. Use the nvidia-container-cli to modify the existing image to install the proper cuda version.

Answer: C

Explanation:
The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.

NEW QUESTION # 240
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 Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.
B. Using the standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
C. Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.
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. Configuring the HPA based on memory 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 # 241
You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN. Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?

A. Ensuring that each tenant has a unique VXLAN Network Identifier (VNI) to isolate their traffic.
B. Using the default MTU size of 1500 bytes for VXLAN traffic.
C. Using the same VNI for all tenants to maximize network utilization.
D. Disabling multicast routing to prevent broadcast traffic.
E. Using the same IP address for all VTEPs to simplify routing.

Answer: A

Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.

NEW QUESTION # 242
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