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最新的 NVIDIA-Certified Professional NCP-AII 免費考試真題:

1. You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A) Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
B) Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
C) The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
D) Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
E) The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).

2. A data scientist reports that training performance on a DGX A100 server has significantly degraded over the past week. 'nvidia-smi' shows all GPUs functioning, but 'nvprof' reveals substantially increased 'cudaMemcpy' times. What is the MOST likely bottleneck?

A) The NVLink connections between GPUs are failing, forcing data transfers through PCle.
B) The GPUs are overheating, causing thermal throttling and slower memory transfers.
C) The CPU is heavily loaded, causing contention for system memory bandwidth.
D) The storage system is slow, delaying data loading and preprocessing.
E) The PCle bus is saturated, limiting data transfer speeds between the CPU and GPUs.

3. You suspect a faulty NVIDIA ConnectX-6 network adapter in a server used for RDMA-based distributed training. Which commands or tools can you use to diagnose potential issues with the adapter's hardware and connectivity?

A) nvsmimonitord to monitor GPU metrics and detect anomalies.
B) ibstat to check the adapter's status, link speed, and active ports.
C) ethtool to examine the adapter's Ethernet settings and statistics.
D) Ispci -v to verify the adapter is detected and its resources are allocated correctly.
E) ping to test basic network connectivity.

4. Your AI training pipeline involves a pre-processing step that reads data from a large HDF5 file. You notice significant delays during this step. You suspect the HDF5 file structure might be contributing to the slow read times. What optimization technique is MOST likely to improve read performance from this HDF5 file?

A) Converting the HDF5 file to a CSV file.
B) Storing the HDF5 file on a network file system like NFS.
C) Compressing the HDF5 file using gzip.
D) Reorganizing the HDF5 file to improve data contiguity and chunking.
E) Encrypting the HDF5 file for enhanced security.

5. You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?

A) Install CUDA 12 inside the Docker container and set the 'CUDA DRIVER VERSION' environment variable to match the host driver version.
B) Mount the CUDA 12 libraries from a separate Docker volume into the container and configure the accordingly.
C) Use a Docker image based on 'nvidia/cuda:12.0-base-ubuntu20.04'. The NVIDIA Container Toolkit will automatically handle the driver compatibility between the host and the container.
D) Upgrade the NVIDIA driver on the host system to the latest version compatible with CUDA 12.
E) Downgrade the application to use CUDA 11.8 to match the host's driver version.

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