COST EFFECTIVE NCP-AII DUMPS & TEST NCP-AII SIMULATOR FREE

Cost Effective NCP-AII Dumps & Test NCP-AII Simulator Free

Cost Effective NCP-AII Dumps & Test NCP-AII Simulator Free

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Quiz 2025 NVIDIA Professional NCP-AII: Cost Effective NVIDIA AI Infrastructure Dumps

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NVIDIA AI Infrastructure Sample Questions (Q153-Q158):

NEW QUESTION # 153
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)

Answer: 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.


NEW QUESTION # 154
After installing NGC CLI using pip, you encounter 'ngc' command not found error even though pip install reported successful. What can be the cause?

  • A. The python executable where NGC CLI got installed is not in the system PATH.
  • B. The host's operating system is not supported by NGC CLI.
  • C. The NGC CLI installation was corrupted. Run 'pip install -force-reinstall nvidia-cli'
  • D. NGC CLI only works inside Docker containers.
  • E. The shell needs to be reloaded or a new terminal session initiated for PATH changes to take effect.

Answer: A,E

Explanation:
The most common reason the 'ngc' command isn't found is that the python environment's executable path isn't in the system PATH (A). A quick fix to ensure environment variables are updated in your current shell is to reload the shell or start a new session (C).


NEW QUESTION # 155
A user reports that their GPU-accelerated application is crashing with a CUDA error related to 'out of memory'. You have confirmed that the GPU has sufficient physical memory What are the likely causes and troubleshooting steps?

  • A. The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
  • B. The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
  • C. The system's virtual memory is exhausted. Increase the swap space.
  • D. The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.
  • E. The application is leaking GPU memory. Use a memory profiling tool like 'cuda-memcheck' to identify the source of the leak.

Answer: B,E

Explanation:
Memory leaks and single-allocation limits are common causes of 'out of memory' errors, even when sufficient physical memory exists. 'cuda-memcheck' is specifically designed to find memory errors in CUDA applications. While driver incompatibility is possible, leaks and allocation size limits are more frequent occurrences.


NEW QUESTION # 156
After replacing a faulty NVIDIA GPU, the system boots, and 'nvidia-smi' detects the new card. However, when you run a CUDA program, it fails with the error "'no CUDA-capable device is detected'". You've confirmed the correct drivers are installed and the GPU is properly seated. What's the most probable cause of this issue?

  • A. The user running the CUDA program does not have the necessary permissions to access the GPU.
  • B. The GPIJ is not properly initialized by the system due to a missing or incorrect ACPI configuration.
  • C. The 'LD LIBRARY PATH* environment variable is not set correctly.
  • D. The new GPU is incompatible with the existing system BIOS.
  • E. The CUDA toolkit is not properly configured to use the new GPU.

Answer: B

Explanation:
The error "no CUDA-capable device is detected", even when 'nvidia-smi' sees the GPIJ, points to a lower-level system issue that prevents CUDA from accessing the card. In such scenarios, ACPI (Advanced Configuration and Power Interface) misconfiguration is frequently the culprit. ACPI handles device initialization and power management. If ACPI doesn't properly configure the new GPU, CUDA programs won't be able to access it. Checking and correcting ACPI configuration would be the first line of action, which includes ensuring proper settings in the system BIOS/IJEFI related to PCI devices, especially those related to GPU/accelerators. LD LIBRARY PATH would affect runtime linking of CUDA libraries, but not the base device detection. User permissions are less likely to be the cause since 'nvidia-smr works.


NEW QUESTION # 157
Which protocol is commonly used in Spine-Leaf architectures for dynamic routing and load balancing across multiple paths?

  • A. BGP (Border Gateway Protocol)
  • B. STP (Spanning Tree Protocol)
  • C. VRRP (Virtual Router Redundancy Protocol)
  • D. OSPF (Open Shortest Path First)
  • E. ECMP (Equal-Cost Multi-Path)

Answer: E

Explanation:
ECMP (Equal-Cost Multi-Path) is crucial for efficiently utilizing the multiple paths available in a Spine-Leaf architecture. It allows traffic to be distributed across these paths, improving throughput and reducing congestion. OSPF and BGP can be used for routing but do not inherently provide per-packet load balancing. STP is used to prevent loops, and VRRP provides router redundancy, neither of which directly address load balancing across multiple equal-cost paths.


NEW QUESTION # 158
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