NCP-AII LATEST TEST LABS, NCP-AII TEST KING

NCP-AII Latest Test Labs, NCP-AII Test King

NCP-AII Latest Test Labs, NCP-AII Test King

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Tags: NCP-AII Latest Test Labs, NCP-AII Test King, NCP-AII Valid Test Sims, NCP-AII Study Test, NCP-AII Reliable Exam Voucher

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NVIDIA AI Infrastructure Sample Questions (Q152-Q157):

NEW QUESTION # 152
You are tasked with optimizing storage performance for a deep learning training job on an NVIDIA DGX server. The training data consists of millions of small image files. Which of the following storage optimization techniques would be MOST effective in reducing I/O bottlenecks?

  • A. Implementing RAID 0 across all storage devices.
  • B. Enabling data compression on the storage volume.
  • C. Using a distributed file system with data striping across multiple storage nodes.
  • D. Implementing a tiered storage system with NVMe drives for frequently accessed data and HDDs for less frequently accessed data.
  • E. Increasing the block size of the file system to the maximum supported value.

Answer: C

Explanation:
A distributed file system with data striping (option B) is the most effective because it parallelizes I/O operations across multiple storage nodes, reducing the load on any single storage device and improving overall throughput for many small files. RAID 0 (A) improves read/write speeds but offers no redundancy. Compression (C) can reduce storage space but adds overhead. Increasing block size (D) is beneficial for large files, but not necessarily for numerous small files. Tiered storage (E) can help, but distributing the file system is the priority for numerous small files.


NEW QUESTION # 153
You need to verify the integrity of the BlueField OS image before flashing it to the SmartNIC. Which method would provide the strongest guarantee that the image has not been tampered with?

  • A. Utilizing gpg to verify the image.
  • B. Checking the MD5 checksum of the image against the published MD5 checksum.
  • C. Comparing the image size with the expected size from the download source.
  • D. Verifying the SHA256 checksum of the image against the published SHA256 checksum.
  • E. Downloading the image from a trusted source.

Answer: A,D

Explanation:
SHA256 provides a stronger collision resistance than MD5, making it a more reliable method for verifying image integrity. Comparing the image size is not a reliable method, as a small change can be made without significantly altering the size. Downloading from a trusted source is important, but doesn't guarantee the image hasn't been tampered with after it was published. GPG provides cryptographic assurance that the file originated from and was signed by an identified party.


NEW QUESTION # 154
An Ai infrastructure relies on a liquid cooling system to dissipate heat from multiple NVIDIA GPUs. After a recent software update, users report intermittent performance degradation and system crashes. You suspect a cooling issue. Which TWO of the following checks are the MOST critical in diagnosing the root cause?

  • A. Examine the ambient temperature in the data center.
  • B. Analyze the system logs for GPU-related errors, specifically those indicating thermal throttling or power capping.
  • C. Check the CPU temperature using 'sensors' command.
  • D. Run a memory test on the host system.
  • E. Verify the pump speed and coolant flow rate within the liquid cooling system.

Answer: B,E

Explanation:
Verifying pump speed and flow rate (A) is crucial for liquid cooling systems. Reduced flow can lead to inadequate cooling and thermal issues. Analyzing system logs for GPU-related errors (C) will directly indicate whether thermal throttling or power capping are occurring, which are common symptoms of cooling problems.


NEW QUESTION # 155
A distributed training job using multiple nodes, each with eight NVIDIA GPUs, experiences significant performance degradation. You notice that the network bandwidth between nodes is consistently near its maximum capacity. However, 'nvidia-smi' shows low GPU utilization on some nodes. What is the MOST likely cause?

  • A. The GPUs are overheating, causing thermal throttling.
  • B. Data is not being distributed evenly across the nodes; some nodes are waiting for data from others.
  • C. The NVIDIA drivers are outdated, causing communication bottlenecks.
  • D. The network interface cards (NICs) are faulty, causing packet loss and retransmissions.
  • E. The CPU is heavily loaded, causing contention for network resources.

Answer: B

Explanation:
High network bandwidth utilization combined with low GPU utilization on some nodes strongly suggests a data imbalance. Some nodes are likely waiting for data from other nodes, causing them to be idle while the network is saturated. This is a common problem in distributed training and requires addressing the data distribution strategy. While other factors (overheating, outdated drivers, faulty NICs, CPU load) could contribute, they are less likely to be the primary cause given the observed symptoms.


NEW QUESTION # 156
You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?

  • A. Monitor GPU memory usage both inside and outside the container using 'nvidia-smi' to identify memory leaks or excessive allocation.
  • B. Set the environment variable inside the container to limit the number of GPUs visible to the application.
  • C. Increase the shared memory size for the container using the '-shm-size' flag when running the container.
  • D. Adjust the environment variable inside the container to ensure consistent GPU ordering.
  • E. Lower the compute capability using '-compute' parameter on docker run.

Answer: A,C

Explanation:
A 'CUDA ERROR OUT OF MEMORY' error can occur due to insufficient shared memory within the container (A). Increasing the shared memory size allows the container to allocate more memory for inter-process communication and GPU data transfers. Monitoring GPU memory usage both inside and outside the container (D) is crucial to identify the source of the memory exhaustion. 'CUDA VISIBLE DEVICES' and (B & C) are primarily used for GPU selection and ordering, not memory management, although limiting GPU visibility could indirectly reduce overall memory consumption if the application is poorly designed and tries to allocate memory on all visible GPUs regardless of need. Lowering compute capability won't directly affect memory usage, although the application will need less memory to process, it might cause issues.


NEW QUESTION # 157
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