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Reliable Exam NCP-AIO Pass4sure - Composite Test NCP-AIO Price

Reliable Exam NCP-AIO Pass4sure - Composite Test NCP-AIO Price

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NVIDIA AI Operations Sample Questions (Q23-Q28):

NEW QUESTION # 23
You are using BCM to manage a Kubernetes cluster with multiple GPU nodes. You need to enable GPU monitoring using Prometheus and the NVIDIA DCGM exporter. Outline the steps required to accomplish this. Choose the correct sequence:

  • A. 0 1. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 2. Install Prometheus in your Kubernetes cluster. 3. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
  • B. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
  • C. 0 1. Install Prometheus in your Kubernetes cluster. 2. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 3. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 4. Verify GPU metrics are available in Prometheus.
  • D. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure the NVIDIA DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
  • E. 0 1. Deploy the NVIDIA DCGM exporter as a Deployment in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.

Answer: C

Explanation:
Prometheus must be installed first to enable metric collection. The DCGM exporter is then deployed as a DaemonSet (to ensure it runs on every node) and configured, enabling Prometheus to scrape the GPU metrics. Finally, the metrics availability is verified.


NEW QUESTION # 24
You're running a large-scale distributed training job using PyTorch and notice that the data loading process is a bottleneck. Your data is stored on an object storage system. Which strategies can you employ to optimize data loading performance, especially considering the distributed nature of the training?

  • A. Implement data caching on the local NVMe drives of each worker node to avoid repeated downloads from the object storage.
  • B. Use a distributed file system (e.g., Lustre, BeeGFS) as an intermediate layer between the object storage and the worker nodes.
  • C. Use PyTorch's 'DataLoader' with a high 'num_workers' value, even if it exceeds the number of CPU cores available.
  • D. Ensure data is stored in a format optimized for parallel reads (e.g., Parquet, Apache Arrow) on the object store.
  • E. Reduce the batch size to minimize the amount of data loaded per iteration.

Answer: A,B,D

Explanation:
Data caching on NVMe drives significantly reduces the need to repeatedly fetch data from object storage. Introducing a distributed filesystem allows a central point where to access the objects. Parquet and Apache Arrow are optimized for columnar data that can be used for parallel access and loading of data from an object store.


NEW QUESTION # 25
Your BCM pipeline uses a custom CUDA kernel. After upgrading the NVIDIA driver, the kernel fails to compile with an obscure error.
What is the MOST likely cause and how do you resolve it?

  • A. The new driver introduced a change in CUDAAPI. Update the kernel code to reflect the API change.
  • B. The driver upgrade corrupted the CUDA runtime. Reinstall the CUDA toolkit.
  • C. A and D
  • D. The custom kernel has a bug. Debug the kernel using cuda-gdb.
  • E. The CUDA toolkit version is incompatible with the new driver. Recompile the kernel with a compatible toolkit.

Answer: C

Explanation:
Driver upgrades can introduce CUDA toolkit incompatibilities or changes in the CUDAAPI that necessitate recompilation or code modification.


NEW QUESTION # 26
What is the primary role of the DOCA Comm Channel service in a DOCA application deployed on a DPU?

  • A. Facilitating high-bandwidth data transfer between the host and the DPU using RDM
  • B. Providing a low-latency communication path for control messages and synchronization signals between the host and the DPU.
  • C. Monitoring the health and performance of the DPLJ and the DOCA application.
  • D. Enabling direct memory access (DMA) between different memory regions within the DPU.
  • E. Offloading network packet processing to the DPU hardware for improved performance.

Answer: B

Explanation:
DOCA Comm Channel is designed for low-latency communication of control messages between the host and the DPU. It's not intended for high-bandwidth data transfers. RDMA is used for data transfers. DMA used to move data between memory spaces within DPU or between Host and DPU. Other option doesn't reflect doca comm channel role.


NEW QUESTION # 27
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

  • A. Enable gang scheduling for the job.
  • B. Configure a lower priority for the job.
  • C. Set a higher quota for the team.
  • D. Set the job's 'restartPolicy' to 'Always'.
  • E. Use Run.ai's 'suspend' and 'resume' commands manually.

Answer: A

Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.


NEW QUESTION # 28
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