PREPARE WELL WITH THE BEST NVIDIA NCP-AII QUESTIONS

Prepare Well With The Best NVIDIA NCP-AII Questions

Prepare Well With The Best NVIDIA NCP-AII Questions

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Tags: NCP-AII Cert Guide, New NCP-AII Exam Sample, NCP-AII Valid Braindumps Questions, Practice NCP-AII Test Online, New NCP-AII Test Pattern

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NVIDIA AI Infrastructure Sample Questions (Q105-Q110):

NEW QUESTION # 105
You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The 'nvidia-smi' output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected. Which of the following could be the cause of this performance bottleneck?

  • A. The host machine's CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.
  • B. The GPU is overheating, causing thermal throttling.
  • C. The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.
  • D. The Docker container is not configured to use shared memory for data transfer with the GPU.
  • E. The application is performing frequent small memory transfers between the CPU and GPIJ.

Answer: A,B,C,E

Explanation:
Several factors could contribute to reduced GPU performance within a Docker container, even if the GPU is being utilized. A heavily loaded CPU (A) can bottleneck data transfer to the GPU. Incompatible CUDA driver versions between host and container (C) cause unexpected errors, and CUDA drivers are important for GPU support. Frequent small memory transfers between CPU and GPU (D) can be inefficient. Overheating (E) can cause the GPU to throttle its performance. While shared memory optimization (B) can help, it's not always the primary cause of the initial performance drop.


NEW QUESTION # 106
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?

  • A. Upgrade the GPUs to models with larger memory capacity.
  • B. Reduce the batch size and concurrency of the offending model in the Triton configuration.
  • C. Disable other models running on the same GPU to free up memory.
  • D. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.
  • E. Increase the system RAM to accommodate the growing memory footprint.

Answer: B,D

Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.


NEW QUESTION # 107
You are tasked with automating the BlueField OS deployment process across a large number of SmartNICs. Which of the following methods is MOST suitable for this task?

  • A. Creating a custom ISO image with the BlueField OS and booting each SmartNIC from a USB drive.
  • B. Manually flashing each SmartNIC using the 'bfboot utility on a workstation.
  • C. Utilizing the 'dd' command to directly copy the image to each SmartNIC's flash memory.
  • D. Using a network boot (PXE) server to deploy the BlueField OS image over the network. This allows centralized management and scalability.
  • E. Utilizing a custom-built python script to flash each individual card, controlled from a central server. This method supports parallel flashing.

Answer: D

Explanation:
PXE boot allows for automated and scalable OS deployment over the network, making it the most suitable option for managing a large number of SmartNlCs. Manually flashing or using USB drives is not practical at scale, and using 'dd' directly can be risky and error-prone without proper checks.


NEW QUESTION # 108
Which of the following techniques can be used to optimize storage performance for deep learning training?

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

Answer: A,B,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 # 109
You are designing a storage solution for a multi-tenant AI cluster. Different teams will be running training jobs concurrently. Which of the following considerations are MOST important for ensuring fair resource allocation and preventing performance bottlenecks?

  • A. Implementing storage quotas and quality-of-service (QOS) policies
  • B. Isolating tenants using separate storage namespaces or volumes
  • C. Using only SSDs for all storage tiers.
  • D. Using a single large storage volume shared by all tenants
  • E. Prioritizing I/O requests from users with higher privileges

Answer: A,B

Explanation:
Storage quotas prevent individual tenants from consuming excessive storage resources, while QOS policies ensure that each tenant receives a fair share of I/O bandwidth. Isolating tenants using separate storage namespaces or volumes prevents noisy neighbor effects, where one tenant's I/O -intensive workload impacts the performance of other tenants. A single large volume doesn't provide isolation. Prioritizing I/O based on privileges is generally not a fair approach in a multi-tenant environment.


NEW QUESTION # 110
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