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NVIDIA AI Operations Sample Questions (Q82-Q87):

NEW QUESTION # 82
You are deploying a containerized application from NGC that relies on the NVIDIA Data Loading Library (DALI) for efficient data preprocessing. You want to ensure that DALI can access the GPU within the container. What steps are necessary to configure DALI correctly?

A. Configure DALI to use the CPU for data preprocessing instead of the GPU.
B. Set the environment variable to specify the GPU to be used by DALI.
C. Use DALI's function to specify the GPU device ID within the DALI pipeline.
D. Install the NVIDIA drivers directly within the container image.
E. Ensure that the NVIDIA Container Toolkit is installed and configured on the host system.

Answer: B,C,E

Explanation:
The NVIDIA Container Toolkit enables GPU access. 'CUDA_VISIBLE DEVICES' controls GPU visibility. specifies the GPU device within the DALI pipeline. A is incorrect; drivers are provided by the host. D defeats the purpose of using DALI for GPU-accelerated data preprocessing.

NEW QUESTION # 83
A distributed BCM pipeline running on multiple nodes exhibits significant performance degradation when scaling to a larger number of nodes. Network bandwidth and storage I/O are not saturated. What's the likely cause?

A. Excessive communication overhead between nodes due to frequent data transfers or synchronization.
B. Inefficient data partitioning across the nodes, leading to uneven workload distribution.
C. All of the above.
D. Insufficient CPU cores or memory on individual nodes, limiting processing capacity.
E. Incorrect configuration of the distributed BCM framework, causing suboptimal task scheduling.

Answer: C

Explanation:
In distributed systems, workload imbalance, communication overhead, resource limitations, and incorrect framework configuration all contribute to scaling issues.

NEW QUESTION # 84
When deploying a DOCA application that utilizes DPDK on a BlueField-2 DPU, what are the key considerations for ensuring optimal performance?

A. Bind DPDK application threads to specific CPU cores to avoid context switching overhead.
B. Use the correct DPDK drivers for the Mellanox ConnectX adapters to enable hardware offload capabilities.
C. Disable all interrupts to avoid context switching and maximize throughput.
D. Disable CPU frequency scaling to ensure consistent performance.
E. Allocate sufficient huge pages to DPDK to minimize TLB misses and improve memory access performance.

Answer: A,B,E

Explanation:
DPDK performance relies on huge pages, CPU affinity, and proper driver usage. CPU frequency scaling should be tuned, not necessarily disabled, and disabling all interrupts is not a feasible solution.

NEW QUESTION # 85
You need to monitor the GPU utilization of pods in your Kubernetes cluster running AI workloads. You want to use Prometheus to collect these metrics. Which of the following approaches is most suitable and efficient for collecting GPU metrics within the Kubernetes environment?

A. Install the 'gpu-exporter' as a Deployment. Configure Prometheus to scrape these endpoints.
B. Manually SSH into each node and run 'nvidia-smi' to record GPU utilization, then input the data into Prometheus.
C. Use the Kubernetes metrics server to collect GPU utilization metrics directly from the kubelet on each node.
D. Run 'nvidia-smi' inside each pod and expose the output as a Prometheus metric using a custom script and the Prometheus client library.
E. Install the 'nvidia-dcgm-exporter' as a DaemonSet to expose GPU metrics from each node. Configure Prometheus to scrape these endpoints.

Answer: E

Explanation:
The correct answer is A. The 'nvidia-dcgm-exporter' is designed to efficiently expose GPU metrics from each node as Prometheus endpoints. Running it as a DaemonSet ensures that it runs on every node with GPUs. Option B is inefficient and requires significant overhead. Option C, the Kubernetes metrics server, does not collect detailed GPU utilization metrics. Option D is a manual and impractical approach. Option E is not standard and relies on an unspecified 'gpu-exporter' , whereas 'nvidia-dcgm-exporter' is an official solution.

NEW QUESTION # 86
The 'nvsm' service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

A. Report the issue to NVIDIA support with the core dump and relevant system information.
B. Disable the NVSwitch on the affected node.
C. Try different versions of CUDA.
D. Recompile the NVSwitch driver with debugging symbols.
E. Increase the swap space on the node.

Answer: A

Explanation:
Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.

NEW QUESTION # 87
......

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Valid NVIDIA NCP-AIO Study Plan | NCP-AIO Detail Explanation

Choosing Valid NCP-AIO Study Plan - Get Rid Of NVIDIA AI Operations

NVIDIA AI Operations Sample Questions (Q82-Q87):

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