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NCP-AIO資訊 & NCP-AIO考試重點

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>> NCP-AIO資訊 <<

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最新的 NVIDIA-Certified Professional NCP-AIO 免費考試真題 (Q15-Q20):

問題 #15
You are trying to configure MIG (Multi-lnstance GPU) on your Run.ai cluster. You have an NVIDIAA100 GPU and want to create two MIG instances, each with 20GB of memory. Assuming the A100 has 80GB of memory, what is the CORRECT MIG profile string you would use when submitting a job to request one of these MIG instances?

A. 2g.10gb
B. 4g.20gb
C. 2g.20gb
D. 1g.5gb
E. 1g.10gb

答案：A

解題說明：
The MIG profile string follows the format 'GPU instances>g.gb'. In this case, '2g.10gb' is the correct MIG profile. This is because the A100 GPU will be split into 2 instances with 10 GB memory each, not 20GB as asked in the question. Even if the A100 has 80GB of memory, MIG is not a 1-1 memory division ratio.

問題 #16
You're deploying a DOCA-based firewall application on a BlueField-2 DPU. The application uses eBPF for packet filtering. What is the primary reason for using eBPF in this scenario?

A. To reduce CPU utilization on the host server by offloading packet filtering to the DPU.
B. To simplify the firewall rule definition using a higher-level language.
C. To improve the compatibility with legacy network devices.
D. To enable dynamic updates to the firewall rules without requiring kernel module recompilation.
E. To automatically generate iptables rules on the host server.

答案：A,D

解題說明：
eBPF allows offloading packet filtering to the DPU, thus reducing the load on the host CPU. It also allows dynamic updates to firewall rules without requiring kernel recompilation, which is a significant advantage in terms of flexibility and maintenance.

問題 #17
You are deploying a VMI container on a cloud platform that supports both NVIDIA vGPU and passthrough GPU access. Your workload requires maximum GPU performance and is not shared with other users. Which GPU access method is generally recommended for this scenario?

A. Shared GPU using CUDA MPS (Multi-Process Service)
B. NVIDIA vGPU
C. It doesn't matter; both vGPU and Passthrough offer the same performance.
D. GPU Passthrough
E. Software Emulation of the GPU

答案：D

解題說明：
GPU passthrough provides direct access to the physical GPU, resulting in the highest possible performance and minimal overhead. vGPU introduces a virtualization layer, potentially reducing performance slightly. CUDA MPS allows for shared GPU access, which is not required in this scenario.

問題 #18
You're configuring MIG on an NVIDIAA100 for a mixed AI/HPC environment. One application requires high memory bandwidth, and the other requires high compute throughput. Which MIG instance configuration would optimally balance these requirements?

A. Create MIG instances with sizes tailored to the applications' specific memory and compute needs, allocating the necessary resources without over-provisioning.
B. Create a single MIG instance and dynamically allocate resources between the two applications.
C. Create one large MIG instance for the high-memory application and a smaller instance for the high-compute application.
D. Disable MIG and allocate the entire GPU to the application with higher priority.
E. Create two identical MIG instances with equal memory and compute resources.

答案：A

解題說明：
Option C is the most flexible and efficient approach. By tailoring MIG instance sizes to each application's specific needs, you can ensure that resources are allocated efficiently, and the overall performance is optimized. Other options may not fully utilize the GPU or may lead to resource contention.

問題 #19
You are managing a Kubernetes cluster running AI training jobs using TensorFlow. The jobs require access to multiple GPUs across different nodes, but inter-node communication seems slow, impacting performance.
What is a potential networking configuration you would implement to optimize inter-node communication for distributed training?

A. Configure a dedicated storage network to handle data transfer between nodes during training.
B. Use InfiniBand networking between nodes to reduce latency and increase throughput for distributed training jobs.
C. Use standard Ethernet networking with jumbo frames enabled to reduce packet overhead during communication.
D. Increase the number of replicas for each job to reduce the load on individual nodes.

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
For distributed AI training jobs that require fast inter-node communication, such as those using TensorFlow across multiple GPUs and nodes,InfiniBand networkingis the preferred solution. InfiniBand provides ultra- low latency and high bandwidth, reducing communication delays significantly and increasing overall training throughput. While jumbo frames on Ethernet can help, they do not match the performance of InfiniBand.
Dedicated storage networks or increasing replicas do not directly address inter-node communication latency.

問題 #20
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NCP-AIO資訊 | NVIDIA AI Operations的便捷資料

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