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NVIDIA AI Infrastructure Sample Questions (Q224-Q229):

NEW QUESTION # 224
You are tasked with installing a DGX A100 server. After racking and connecting power and network cables, you power it on, but the BMC (Baseboard Management Controller) is not accessible via the network. You have verified the network cable is connected and the switch port is active. What are the MOST likely causes and initial troubleshooting steps you should take?

A. The BMC IP address is not configured or is on a different subnet. Check the BMC's network configuration using the DGX's front panel or via serial console. Verify DHCP is enabled and functioning or manually configure a static IP address.
B. The network switch port is not configured for the correct VLAN. Verify the switch port configuration to ensure it is on the same VLAN as the BMC.
C. The BMC is not powered on because the main power supply is faulty. Verify the power supply LEDs are lit and providing power to the system.
D. The BMC is faulty and needs to be replaced. Contact NVIDIA support for RMA.
E. The BMC firmware is corrupted and needs to be reflashed using a USB drive. Check the DGX support site for the latest BMC firmware.

Answer: A,B

Explanation:
The most likely causes are network configuration issues (incorrect IP, subnet, or VLAN). The BMC requires a valid IP configuration and network connectivity to be accessible. While other options are possible, they are less common as initial causes.

NEW QUESTION # 225
Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

A. Applying thermal paste to the GPU memory chips.
B. Ensuring the server room temperature is kept below 25 degrees Celsius.
C. Increasing the fan speed of the server chassis fans to maximum.
D. Optimizing airflow to ensure hot air is efficiently exhausted from the rack and cool air is drawn in.
E. Using liquid cooling for the CPUs, but air cooling for the GPUs.

Answer: D

Explanation:
While ambient temperature is important, optimized airflow is crucial for removing the heat generated by the GPUs. Focusing solely on CPU cooling neglects the GPU heat. Applying thermal paste to memory chips is generally unnecessary unless specifically recommended by the manufacturer. Maximizing fan speed can help, but efficient airflow design is more effective.

NEW QUESTION # 226
Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?

A. Using labels and selectors to schedule workloads on specific GPU nodes.
B. Using taints and tolerations to dedicate GPU nodes to specific workloads.
C. Using node affinity only.
D. Using pod affinity and anti-affinity rules to control pod placement.
E. Using namespaces with resource quotas and network policies.

Answer: E

Explanation:
Namespaces provide logical isolation within a Kubernetes cluster. Resource quotas limit the resources (including GPIJs) that a namespace can consume, while network policies control network traffic between namespaces, ensuring strong isolation. Options B, C, D, and E provide some level of control over pod placement but do not offer the same level of resource and network isolation as namespaces with resource quotas and network policies.

NEW QUESTION # 227
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
B. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
C. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
D. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
E. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.

Answer: A,B,C,E

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

NEW QUESTION # 228
You're deploying BlueField OS to multiple SmartNICs with varying hardware revisions. How can you ensure that the correct device tree is loaded for each specific SmartNIC during the boot process?

A. Create a single device tree that is compatible with all SmartNIC revisions. The kernel will automatically handle compatibility.
B. Use a bootloader (e.g., U-Boot) that can detect the hardware revision and load the appropriate device tree based on a predefined mapping.
C. Embed the device tree within the kernel image. The kernel will automatically select the correct one during boot.
D. Relocate the device tree after the OS is running, but before services are started using a custom script.
E. Manually specify the device tree file in the bootloader configuration for each SmartNI

Answer: E

Explanation:
A bootloader like U-Boot is designed to handle hardware detection and conditional loading of resources like device trees. It can identify the SmartNIC revision and load the corresponding DTB file. Creating a single compatible DTB is difficult and may not fully utilize hardware capabilities. Manually specifying the DTB for each NIC is not scalable. Embedding the DTB in the kernel is uncommon. Attempting to modify the device tree at runtime could lead to instability.

NEW QUESTION # 229
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New NCP-AII Mock Exam & Interactive NCP-AII Practice Exam

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NVIDIA AI Infrastructure Sample Questions (Q224-Q229):

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