Free EMC NCP-AII Exam Dumps Questions & Answers

You are designing a storage system using BeeGFS for an AI cluster. The cluster consists of 10 client nodes, each with 2 NVIDIAAIOO GPUs, and 4 storage servers. Each storage server has 10 NVMe SSDs. The training dataset is 100TB. You want to ensure high availability and performance. Which of the following BeeGFS configurations would be MOST appropriate?

• A.One MDS server and 10 OSTs, splitting each NVMe SSD into smaller virtual OSTs.
• B.Two MDS servers in a high-availability configuration and 40 OSTs (one per NVMe SSD).
• C.A single metadata server (MDS) and four storage targets (OSTs), each spanning all 10 NVMe SSDs on a storage server.
• D.10 MDS server (one per client nodes) and a single OST.
• E.Four MDS servers (one per storage server) and 40 OSTs (one per NVMe SSD).

You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?

• A.A driver bug causing incorrect workload distribution.
• B.A hardware fault within the GPU, such as a memory error or core failure.
• C.The GPU's compute mode is set to 'Exclusive Process'.
• D.Insufficient system memory preventing data transfer to that GPU.
• E.An incorrect CUDA version installed.

You're deploying a large language model for inference using NVIDIA Triton Inference Server. You need to validate that the server can handle the expected query load while maintaining acceptable latency. Which tools and metrics are most relevant for this validation?

• A.All of the above
• B.Check CPU utilization using 'top'
• C.Employ a load testing tool (e.g., 'locust' , 'JMeter') to simulate client requests and measure response times.
• D.Use 'nvidia-smi' to monitor GPIJ utilization and memory usage.
• E.Utilize Triton's built-in metrics endpoint to track latency, throughput, and request queue length.

A user reports that their GPU-accelerated application is crashing with a CUDA error related to 'out of memory'. You have confirmed that the GPU has sufficient physical memory What are the likely causes and troubleshooting steps?

• A.The system's virtual memory is exhausted. Increase the swap space.
• B.The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
• C.The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
• D.The application is leaking GPU memory. Use a memory profiling tool like 'cuda-memcheck' to identify the source of the leak.
• E.The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.

An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled. What are the THREE most likely root causes of these crashes?

• A.GPIJ memory errors that are not detectable by standard CPU-based memory tests.
• B.Incompatible NVIDIA driver version with the installed Linux kernel.
• C.A faulty power supply unit (PSU) that is unable to provide stable power under peak load.
• D.Insufficient airflow within the server, leading to overheating of the GPUs and VRMs.
• E.Network congestion causing intermittent data corruption during distributed training.