NCP-AIO REAL EXAM ANSWERS - NEW NCP-AIO BRAINDUMPS

NCP-AIO Real Exam Answers - New NCP-AIO Braindumps

NCP-AIO Real Exam Answers - New NCP-AIO Braindumps

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Tags: NCP-AIO Real Exam Answers, New NCP-AIO Braindumps, NCP-AIO Practice Test Online, Reliable NCP-AIO Test Notes, NCP-AIO Exam Flashcards

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Pass Guaranteed Quiz 2025 NVIDIA High Hit-Rate NCP-AIO: NVIDIA AI Operations Real Exam Answers

NVIDIA is one of the most powerful and rapidly growing fields nowadays. Everyone is trying to get the NVIDIA NCP-AIO certification to improve their futures with it. Success in the test plays an important role in the up gradation of your CV and getting a good job or working online to achieve your dreams. The students are making up their minds for the NVIDIA NCP-AIO test but they are mostly confused about where to prepare for it successfully on the first try. This confusion leads to choosing outdated material and ultimately failure in the test. The best way to avoid failure is using updated and real questions.

NVIDIA AI Operations Sample Questions (Q41-Q46):

NEW QUESTION # 41
You are tasked with designing a data center network for AI workloads that must support both RDMA over Converged Ethernet (RoCEv2) and traditional TCP/IP traffic. How should you configure the network to ensure optimal performance for both types of traffic?

  • A. Implement Priority Flow Control (PFC) and Explicit Congestion Notification (ECN) to prevent packet loss and congestion for RoCEv2 traffic.
  • B. Allocate a larger MTU to TCP/IP traffic and small MTU to RoCEv2 traffic.
  • C. Configure all network switches with the default settings.
  • D. Disable QOS (Quality of Service) to treat all traffic equally.
  • E. Use separate physical networks for RoCEv2 and TCP/IP traffic.

Answer: A

Explanation:
RoCEv2 is sensitive to packet loss and congestion. PFC and ECN are essential mechanisms to ensure reliable and high- performance RoCEv2 communication on a converged Ethernet network. Disabling QOS treats all traffic equally, which can starve RoCEv2. Using separate networks adds complexity and cost. Default settings are unlikely to be optimized for RoCEv2. PFC prevents packet loss due to congestion, and ECN provides feedback to sources to slow down before congestion occurs. Large MTUs are beneficial for both but not the primary config.


NEW QUESTION # 42
You are deploying a multi-GPU training job using a container from NGC on a Slurm cluster. The container expects the number of GPUs to be available in the 'CUDA VISIBLE DEVICES' environment variable. How do you ensure this variable is correctly set within the Slurm job script?

  • A. Use the Slurm command 'srun' with the '-gpus' option to allocate GPUs and automatically set
  • B. Utilize the Slurm environment variable 'SLURM JOB GPUS' to dynamically set 'CUDA_VISIBLE DEVICES' in the job script (e.g., 'export
  • C. Configure the NVIDIA Container Toolkit to automatically detect and set 'CUDA VISIBLE DEVICES'.
  • D. Set the environment variable manually in the Slurm job script to a fixed value (e.g.,
  • E. Define the "CUDA VISIBLE DEVICES' environment variable in the containers Docket-file.

Answer: A,B

Explanation:
B and D are correct. 'srun -gpus' handles GPU allocation and sets the environment variable. 'SLURM JOB GPUS provides a dynamic way to access allocated GPUs within the script. A is incorrect as it doesn't adapt to the actual allocation. C is incorrect because it's not a Slurm configuration. E depends on the specific toolkit version and might not be reliable without explicit configuration in the job script.


NEW QUESTION # 43
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?

  • A. nvidia-cuda-smi
  • B. nvcc
  • C. nvprof
  • D. nvidia-container-cli
  • E. nvidia-smi

Answer: E

Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.


NEW QUESTION # 44
A data science team is using Fleet Command to deploy AI models to edge devices in a smart city project. They've noticed that some devices are consistently failing to update due to insufficient disk space. Which of the following is the MOST effective strategy to mitigate this issue?

  • A. Ignore the failing devices and focus on the ones that are updating successfully.
  • B. Increase the disk space on all edge devices remotely via Fleet Command.
  • C. Optimize the deployed models to reduce their size, and update the affected devices only.
  • D. Roll back the updates to the previous version for all devices.
  • E. Implement a process to automatically clean up unused files and data on the edge devices before each update.

Answer: E

Explanation:
Optimizing models (B) is helpful, but a cleanup process (E) addresses the root cause. Increasing disk space (A) might not be feasible or cost-effective. Ignoring devices (C) is unacceptable. Rolling back updates (D) is a temporary solution. Thus, automatically cleaning up unused files is the most proactive and sustainable approach.


NEW QUESTION # 45
Which of the following are key considerations when selecting storage for an AI training workload using large datasets? (Select TWO)

  • A. CPU core count on the storage server.
  • B. Network bandwidth between storage and compute nodes.
  • C. IOPS (Input/Output Operations Per Second).
  • D. RAM on the storage server.
  • E. Storage capacity.

Answer: B,C,E

Explanation:
Capacity is obviously important for large datasets. IOPS and Network Bandwidth are critical for feeding data to the GPUs efficiently and avoiding bottlenecks during training. While CPU and RAM on storage servers are relevant, they are secondary to capacity, IOPS, and network performance. Insufficient bandwidth can lead to GPU starvation, significantly slowing down the training process.


NEW QUESTION # 46
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Exam Questions For NVIDIA NCP-AIO With Reliable Answers

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