NEW NVIDIA NCP-AIO TEST BOOTCAMP | NCP-AIO EXAM FEES

New NVIDIA NCP-AIO Test Bootcamp | NCP-AIO Exam Fees

New NVIDIA NCP-AIO Test Bootcamp | NCP-AIO Exam Fees

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Tags: New NCP-AIO Test Bootcamp, NCP-AIO Exam Fees, Valid Dumps NCP-AIO Ppt, Dumps NCP-AIO PDF, Free NCP-AIO Pdf Guide

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NVIDIA AI Operations Sample Questions (Q60-Q65):

NEW QUESTION # 60
A data scientist complains that their GPU-accelerated inference service is intermittently failing with CUDA errors. After checking logs, you notice 'CUDA out of memory' errors. What are the MOST effective strategies to mitigate this issue?

  • A. Implement GPU memory pooling or sharing strategies.
  • B. Reduce the batch size for inference requests.
  • C. Upgrade the server's CPU.
  • D. Increase the batch size for inference requests.
  • E. Reduce the model size (e.g., using quantization or pruning).

Answer: A,B,E

Explanation:
Reducing model size directly decreases the memory footprint. GPU memory pooling allows multiple processes to share the available GPU memory more efficiently. Reducing batch size reduces the memory required for each inference request, which can prevent OOM errors. Increasing batch size would likely exacerbate the problem. Upgrading the CPU would not directly solve GPU memory issues.


NEW QUESTION # 61
Which BCM configuration file defines the operating system image used for provisioning new nodes?

  • A. image.yaml
  • B. cluster.yaml
  • C. bcm.conf
  • D. node.yaml
  • E. provisioning.yaml

Answer: B

Explanation:
The 'cluster.yamr file in BCM typically contains the definition of the operating system image, including the URL or path to the image file, along with other cluster-level configuration details like network settings and Kubernetes version.


NEW QUESTION # 62
You are using NVSHMEM for a large-scale simulation. The application is crashing with segmentation faults. After checking the code for memory errors, you suspect an issue with NVSHMEM configuration. Which of the following environment variables is MOST likely to be misconfigured and causing the crashes?

  • A. NCCL DEBUG
  • B. CUDA DEVICE ORDER
  • C. CUDA VISIBLE DEVICES
  • D. LD LIBRARY PATH
  • E. NVSHMEM SYMMETRIC SIZE

Answer: E

Explanation:
NVSHMEM SYMMETRIC SIZE defines the size of the symmetric heap, which is the shared memory region accessible by all processes. If this value is too small, it can lead to segmentation faults when the application tries to allocate more memory than available. The other variables are less directly related to memory allocation within the NVSHMEM environment. While CUDA VISIBLE DEVICES affects GPU visibility, it won't cause segmentation faults related to symmetric memory allocation. LD_LIBRARY_PATH is for finding libraries, not memory. NCCL_DEBUG controls debugging output. CUDA DEVICE_ORDER affects device enumeration.


NEW QUESTION # 63
You are designing storage for an AI data center focused on training large language models (LLMs). You need to optimize for both capacity and speed. Which storage technology is most suitable for the training data itself, considering the need for high throughput and parallel access?

  • A. Object storage (e.g., AWS S3, Ceph) accessed over the internet
  • B. Traditional Hard Disk Drives (HDDs) in a RAID 5 configuration
  • C. Tape storage
  • D. Network File System (NFS) over a 1 Gbps network
  • E. NVMe-based parallel file system (e.g., BeeGFS, Lustre) directly attached to compute nodes

Answer: E

Explanation:
NVMe-based parallel file systems offer the highest throughput and lowest latency, crucial for feeding data to GPUs during LLM training. HDDs and NFS have significant performance bottlenecks, object storage is not optimized for the access patterns of training, and tape is for archival, not active use.


NEW QUESTION # 64
What is the primary advantage of using a disaggregated infrastructure for AI workloads compared to a traditional converged infrastructure?

  • A. Simplified management and monitoring.
  • B. Reduced power consumption.
  • C. Better security due to isolation.
  • D. Lower initial capital expenditure.
  • E. Independent scaling of compute, storage, and networking resources.

Answer: E

Explanation:
Disaggregated infrastructure allows you to scale compute, storage, and networking independently based on the specific needs of your AI workloads. Converged infrastructure typically scales in predefined units, which can lead to resource wastage. While disaggregation can lead to other benefits, independent scaling is the primary advantage for AI workloads with varying resource demands.


NEW QUESTION # 65
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