100% PASS 2025 NCP-AII: NVIDIA AI INFRASTRUCTURE–TRUSTABLE FREE BRAIN DUMPS

100% Pass 2025 NCP-AII: NVIDIA AI Infrastructure–Trustable Free Brain Dumps

100% Pass 2025 NCP-AII: NVIDIA AI Infrastructure–Trustable Free Brain Dumps

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NVIDIA AI Infrastructure Sample Questions (Q192-Q197):

NEW QUESTION # 192
You've installed the NGC CLI and successfully configured your API key However, when running 'ngc registry model download' , you receive a 'Permission denied' error despite having valid credentials. What are possible causes and solutions?

  • A. The model you are trying to download requires acceptance of a separate end-user license agreement (EULA) that you haven't yet accepted.
  • B. Models can only be downloaded to specific location.
  • C. Your API key lacks the necessary permissions to download models from the NGC registry. Contact your NVIDIA organization administrator.
  • D. The user account running the 'ngc' command does not have write permissions to the destination directory specified for the download.
  • E. There is an issue with network connectivity or firewall rules preventing access to the NGC registry. Verify network connectivity and firewall rules.

Answer: A,C,D,E

Explanation:
The 'Permission denied' error can stem from file system permissions (A), insufficient API key permissions (B), unaccepted EULAs (C), or network/firewall issues (D). All these must be considered before assuming a CLI installation problem. Option E is incorrect.


NEW QUESTION # 193
Which of the following techniques can be used to optimize storage performance for deep learning training?

  • A. Data compression using lossless algorithms (e.g., gzip)
  • B. Data sharding
  • C. Using a larger block size for the file system
  • D. Data deduplication
  • E. Data prefetching

Answer: B,C,E

Explanation:
Data prefetching anticipates future data needs and loads data into the cache before it is requested. A larger block size can improve I/O throughput for large files. Data sharding distributes data across multiple storage devices to increase parallelism. Data compression, while saving space, can add overhead during training. Data deduplication is not normally usefull for training data sets.


NEW QUESTION # 194
Which of the following statements accurately describe the benefits of using MIG (Multi-lnstance GPU) in an AI/HPC environment?
(Select all that apply)

  • A. MIG guarantees performance isolation between different workloads running on the same GPIJ.
  • B. MIG eliminates the need for GPU virtualization software.
  • C. MIG allows a single GPU to be partitioned into multiple isolated instances, improving resource utilization.
  • D. MIG allows running multiple CUDA versions simultaneously on the same physical GPIJ.
  • E. MIG increases the overall FLOPS of the GPU.

Answer: A,C

Explanation:
MIG allows partitioning a single GPU into multiple isolated instances, thus optimizing resource utilization (A). It provides hardware- level isolation, ensuring performance consistency and preventing interference between workloads (B). While MIG simplifies GPU sharing, it doesn't eliminate the need for all virtualization software, especially in more complex environments. MIG doesn't increase FLOPS (D); it divides the existing compute power. While different containers running on different MIG instances could have different CUDA versions, MIG itself doesn't directly handle this (E); this is handled via containers or environments.


NEW QUESTION # 195
You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?

  • A. eBPF (extended Berkeley Packet Filter) with XDP (eXpress Data Path).
  • B. Netfilter with connection tracking.
  • C. OVS (Open vSwitch) with OpenFlow rules.
  • D. IPsec (Internet Protocol Security) tunnels.
  • E. TC (Traffic Control) with 'iptables' rules.

Answer: A

Explanation:
eBPF with XDP is the most suitable technology for deep packet inspection (DPI) on BlueField. It allows you to run custom code at near-line speed to inspect packets and apply security rules based on application signatures. TC and Netfilter are less efficient for DPI, OVS/OpenFlow are more for switching policies, and IPsec focuses on encryption.


NEW QUESTION # 196
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

  • A. Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.
  • B. Enabling CUDA graph capture to reduce kernel launch overhead.
  • C. Reducing the model's memory footprint by using quantization or pruning techniques.
  • D. Increasing the GPU's TCC (Tesla Compute Cluster) mode priority.
  • E. Optimize the model using TensorRT.

Answer: D

Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.


NEW QUESTION # 197
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