EXAM NCP-AIO REVIEWS - 100% PASS 2025 FIRST-GRADE NVIDIA NCP-AIO ONLINE TRAINING MATERIALS

Exam NCP-AIO Reviews - 100% Pass 2025 First-grade NVIDIA NCP-AIO Online Training Materials

Exam NCP-AIO Reviews - 100% Pass 2025 First-grade NVIDIA NCP-AIO Online Training Materials

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NVIDIA AI Operations Sample Questions (Q34-Q39):

NEW QUESTION # 34
You are troubleshooting slow training times for a deep learning model. You suspect the storage is the bottleneck. You are using a network file system (NFS) to serve the dat a. Which of the following NFS mount options would most likely improve performance for read- heavy workloads?

  • A. Increasing the 'rsize' and 'wsize' to the maximum supported values by the NFS server and client.
  • B. Using the 'sync' mount option.
  • C. Using the 'async' mount option.
  • D. Reducing the 'rsize' (read size) and 'wsize' (write size) to the smallest possible value.
  • E. Mounting the NFS share with the 'nolock' option.

Answer: A,C

Explanation:
The 'async' mount option allows the client to write data to its cache and acknowledge the write before the data is actually written to the server, improving write performance. Increasing 'rsize' and 'wsize' allows for larger reads and writes, potentially improving throughput for large files commonly used in AI/ML datasets. However, exceeding the server's supported values will not help and may hurt performance.


NEW QUESTION # 35
You are using Fleet Command to manage AI model deployments to a diverse fleet of edge devices with varying hardware capabilities.
Some devices are equipped with GPUs, while others rely on CPUs for inference. How can you ensure that the correct version of the AI model is deployed to each device type?

  • A. Manually select the appropriate model version for each device during deployment.
  • B. Develop a custom script to determine device capabilities and deploy models accordingly.
  • C. Create separate Fleet Command organizations for each device type.
  • D. Use Fleet Command's device targeting feature with appropriate labels to define deployment rules based on hardware capabilities.
  • E. Deploy the same model version to all devices and rely on the devices to automatically adapt to their hardware.

Answer: D

Explanation:
Device targeting with labels is the most efficient and scalable way to manage deployments to diverse hardware. Separate organizations (A) are overly complex. Manual selection (C) is error-prone. Relying on automatic adaptation (D) might not be reliable. Custom scripts (E) add unnecessary complexity when Fleet Command provides built-in features.


NEW QUESTION # 36
You are responsible for deploying a deep learning model for real-time inference using Triton Inference Server from NGC. Latency is a critical requirement. Which of the following optimization techniques can you employ to minimize inference latency?

  • A. Disable CUDA graphs to improve CPU utilization.
  • B. Reduce the model's precision (e.g., from FP32 to FP16 or INT8).
  • C. Optimize the model's architecture and code for GPU execution.
  • D. Use dynamic batching to aggregate multiple inference requests into a single batch.
  • E. Increase the number of instances of the model deployed on the Triton server.

Answer: B,C,D,E

Explanation:
A, B, C and E are correct. Increasing instances allows for parallel processing. Dynamic batching improves throughput. Reducing precision accelerates computation. Model optimization enhances GPU utilization. D is generally incorrect; CUDA graphs typically improve performance by reducing kernel launch overhead.


NEW QUESTION # 37
A research team wants to use a specific version of TensorFlow (e.g., TensorFlow 2.9.0) for their experiments within the Run.ai environment. What is the RECOMMENDED approach for ensuring this specific TensorFlow version is available to their jobs?

  • A. Specify the TensorFlow version in the Run.ai job definition using a 'tf-version' parameter.
  • B. Install TensorFlow 2.9.0 directly on each node in the cluster.
  • C. Create a custom Docker image with TensorFlow 2.9.0 pre-installed and use that image for the Run.ai jobs.
  • D. Use Run.ai's built-in environment module system to load TensorFlow 2.9.0.
  • E. Mount a shared network drive containing TensorFlow 2.9.0 libraries into each container.

Answer: C

Explanation:
Creating a custom Docker image with the desired TensorFlow version (2.9.0 in this case) is the recommended approach. This ensures that the job has a consistent and reproducible environment, regardless of the underlying infrastructure. Installing directly on nodes creates management overhead and potential conflicts. Run.ai does not have a built-in tf-version parameter or environment module system for this purpose. Mounting a network drive is less reliable and can introduce performance issues.


NEW QUESTION # 38
You are a Solutions Architect designing a data center infrastructure for a cloud-based AI application that requires high-performance networking, storage, and security. You need to choose a software framework to program the NVIDIA BlueField DPUs that will be used in the infrastructure. The framework must support the development of custom applications and services, as well as enable tailored solutions for specific workloads.
Additionally, the framework should allow for the integration of storage services such as NVMe over Fabrics (NVMe-oF) and elastic block storage.
Which framework should you choose?

  • A. NVIDIA TensorRT
  • B. NVIDIA CUDA
  • C. NVIDIA NSight
  • D. NVIDIA DOCA

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIADOCA(Data Center Infrastructure-on-a-Chip Architecture) is the software framework designed to program NVIDIA BlueField DPUs (Data Processing Units). DOCA provides libraries, APIs, and tools to develop custom applications, enabling users to offload, accelerate, and secure data center infrastructure functions on BlueField DPUs.
DOCA supports integration with key data center services including storage protocols such asNVMe over Fabrics (NVMe-oF), elastic block storage, and network security and telemetry. It enables tailored solutions optimized for specific workloads and high-performance infrastructure demands.
* TensorRT is focused on AI inference optimization.
* CUDA is NVIDIA's GPU programming model for general-purpose GPU computing, not for DPUs.
* NSight is a development environment for debugging and profiling NVIDIA GPUs.
Therefore,NVIDIA DOCAis the correct framework for programming BlueField DPUs in a data center environment requiring custom application development and advanced storage/networking integration.


NEW QUESTION # 39
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