Practice Free NCP-AII Exam Online Questions
You are deploying a BlueField-2 DPU-based server in a VMware vSphere environment.
Which network virtualization technology is most commonly used in conjunction with the DPU to provide accelerated networking and security features within the virtualized environment?
- A . VXLAN (Virtual Extensible LAN)
- B . GRE (Generic Routing Encapsulation)
- C . IPsec (Internet Protocol Security)
- D . SR-IOV (Single Root 1/0 Virtualization)
- E . LACP (Link Aggregation Control Protocol)
A
Explanation:
VXLAN is a widely adopted network virtualization technology that is frequently used with BlueField DPUs in vSphere environments. DPUs can offload VXLAN encapsulation and decapsulation, improving performance and reducing the CPU load on the host. SR-IOV provides direct access to the NIC for VMs, but it’s not a network virtualization technology in the same sense as VXLAN. GRE and IPsec are tunneling protocols but less common in vSphere for this specific use case. LACP is for link aggregation, not virtualization.
You are installing four NVIDIAAIOO GPUs in a server, and after installation, you observe that the PCle link speed for one of the GPUs is running at x8 instead of the expected x16.
What could be the POSSIBLE causes for this reduced PCle link speed?
- A . The GPU is faulty.
- B . The CPU does not have enough PCle lanes to support all GPUs at x16.
- C . The PCle slot is only wired for x8 speed.
- D . The BIOS/UEFI is configured to limit the PCle link speed for that slot.
- E . All of the above
E
Explanation:
A reduced PCle link speed can result from multiple factors: a faulty GPU, insufficient PCle lanes from the CPU, the physical wiring of the PCle slot, or a BIOS/IJEFI configuration limiting the speed. All these are potentially viable cause. Thus answer E is correct.
Which of the following is a primary benefit of using a CLOS network topology (e.g., Spine-Leaf) in a data center?
- A . Reduced capital expenditure (CAPEX)
- B . Increased network diameter
- C . Improved scalability and bandwidth utilization
- D . Simplified network management
- E . Enhanced security
C
Explanation:
CLOS networks like Spine-Leaf provide excellent scalability due to their non-blocking architecture, allowing for increased bandwidth utilization and easy expansion. CAPEX might be higher due to more switches. The network diameter can be larger compared to traditional topologies. While CLOS networks can be managed effectively, the management complexity can be higher. Security benefits are not a primary characteristic of the CLOS topology itself.
You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself.
Assuming you have network access to the server’s BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?
- A . SNMP (Simple Network Management Protocol) with MIB (Management Information Base)
- B . HTTP with JSON
- C . SSH with plain text output from ‘nvidia-smi’
- D . IPMI (Intelligent Platform Management Interface) with SDR (Sensor Data Records)
- E . Syslog with CSV (Comma-separated Values)
D
Explanation:
IPMI is a standard interface for out-of-band server management, commonly used for monitoring hardware sensors like temperature and utilization. BMCs typically support IPMI. SDRs are the data format used by IPMI for sensor data. SNMP is also an option, but IPMI is more directly tied to hardware monitoring. The rest are less efficient or require additional software installation.
A system administrator noticed a failure on a DGX H100 server. After a reboot, only the BMC is available.
What could be the reason for this behavior?
- A . The network card has no link / connection.
- B . A boot disk has failed.
- C . Multiple GPUs have failed.
- D . There are more than two failed power supplies.
D
Explanation:
On an NVIDIA DGX system, the Baseboard Management Controller (BMC) is an independent processor that runs even if the main CPU and Operating System fail to load. If a server reboots and the administrator can access the BMC web interface or IPMI console, but the OS (Ubuntu/DGX OS) does not load, the most likely cause is a boot disk failure. The DGX H100 uses NVMe drives in a RAID-1 configuration for the OS boot volume. If both drives in the mirror fail, or if the boot partition becomes corrupted, the system will hang at the BIOS or UEFI prompt, unable to find a bootable device. While failed power supplies (Option D) or network links (Option A) can cause issues, they would typically prevent the BMC from being reachable at all or prevent remote network traffic respectively. A GPU failure (Option C) would not stop the OS from booting; the system would simply boot with a degraded GPU count. Therefore, checking the storage health via the BMC "Storage" logs is the correct diagnostic step.
During NVLink Switch configuration, you encounter issues where certain GPUs are not being recognized by the system.
Which of the following troubleshooting steps are most likely to resolve this problem?
- A . Verify that all NVLink cables are securely connected and properly seated.
- B . Check the system BIOS settings to ensure that NVLink is enabled and configured correctly.
- C . Ensure that the NVLink Switch firmware is compatible with the installed GPUs.
- D . Reinstall the operating system.
- E . Check the Power supply for enough capacity and stability.
A,CE
Explanation:
Physical connection issues (A), BIOS configuration (B), and firmware incompatibility (C) are the most common causes of GPUs not being recognized. Reinstalling the operating system is a drastic measure that is unlikely to solve the problem. Checking the Power supply may also required to ensure the complete system have enough capacity and stability.
You are installing a GPU server in a data center with limited cooling capacity.
Which of the following server configuration choices would BEST help minimize the server’s thermal output, without significantly compromising performance? Assume all options are compatible.
- A . Choose GPUs with a lower TDP (Thermal Design Power), even if it means using older generation GPUs.
- B . Use a passively cooled CPU to reduce fan noise and power consumption.
- C . Configure the BIOS/UEFI to aggressively throttle CPU and GPU frequencies under heavy load.
- D . Implement liquid cooling for the GPUs and CPUs.
- E . Increase the ambient temperature of the data center to reduce the temperature differential.
D
Explanation:
Liquid cooling is the most effective way to remove heat from high-power components like GPUs and CPUs, allowing them to operate at their maximum performance without overheating. Choosing lower TDP GPUs will reduce thermal output but will also significantly reduce performance. Throttle frequency is useful, but liquid cooling enables optimal performance within thermal constraints. Data center should reduce cooling cost but is counter intuitive to reduce server temparature.
After physically installing a new NVIDIA GPU in a server, you boot the system. You notice that the GPU is not recognized by the operating system. You’ve verified the card is properly seated and powered.
What are the MOST LIKELY causes and solutions? (Select TWO)
- A . The incorrect GPU drivers are installed or no drivers are installed at all. Solution: Download and install the latest drivers from the NVIDIA website.
- B . The motherboard BIOS/UEFI does not support the GPU. Solution: Update the motherboard BIOS/UEFI to the latest version.
- C . The PCIe slot is faulty. Solution: Try installing the GPU in a different PCIe slot.
- D . The GPU is not compatible with the operating system. Solution: Reinstall the operating system.
- E . The GPU is defective. Solution: Return the GPU to the manufacturer.
A,B
Explanation:
The most common reasons for a GPU not being recognized are missing or incorrect drivers and an outdated BIOS/UEFI that doesn’t support the card. A faulty PCIe slot is possible, but less likely as the initial troubleshooting step. Reinstalling the OS is rarely needed for driver issues. A defective GPU is possible but should be considered after other options are exhausted.
You are setting up a multi-GPU AI server for deep learning. You want to ensure optimal inter-GPU communication.
Which of the following interconnect technologies would provide the BEST performance?
- A . PCle Gen3 x16
- B . PCle Gen4 x16
- C . NVLink
- D . Ethernet
- E . Infiniband
C
Explanation:
NVLink is designed specifically for high-bandwidth, low-latency inter-GPU communication, offering significantly better performance than PCIe or network connections for workloads that benefit from it. InfiniBand is suitable for node to node communication, while NVLink is for GPU to GPU on the same node.
You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough.
How can you reduce the load on the CPU and improve the overall training throughput?
- A . Move the training data to a local NVMe drive on the training node.
- B . Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
- C . Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.
- D . Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.
- E . Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.
C
Explanation:
Using NVIDIA DALI (option C) allows you to offload data augmentation and preprocessing tasks from the CPU to the GPU, freeing up CPU resources for other tasks and enabling faster data loading. Moving to a local NVMe drive (A) bypasses BeeGFS but doesn’t address the CPU bottleneck. Increasing MDSs (B) improves metadata performance but doesn’t directly help with the CPU-bound data augmentation. Decreasing the batch size (D) reduces the workload but doesn’t solve the underlying CPU bottleneck. Data compression (E) can increase CPU load due to the decompression process.