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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workload Management | 20% | - Framework and runtime configuration - Data pipeline and storage integration - AI workload deployment and scaling - Job scheduling and queue management |
| Topic 2: Troubleshooting and Optimization | 20% | - Throughput and latency optimization - System health and reliability maintenance - Fault diagnosis and resolution - Performance monitoring and analysis |
| Topic 3: Management | 28% | - Software and container lifecycle management - Access control and security policies - Resource allocation and scheduling - GPU cluster and compute node management |
| Topic 4: Installation and Deployment | 32% | - Container orchestration and resource management - Driver and firmware installation - AI cluster setup and configuration - NVIDIA software stack deployment |
>> NCP-AIO Exam Questions Vce <<
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NEW QUESTION # 85
Which of the following network technologies would you prioritize for connecting storage arrays to GPU servers in an AI data center to minimize latency for data access?
Answer: B
Explanation:
NVMe-oF using RDMA (Remote Direct Memory Access) offers the lowest latency and highest throughput for accessing storage over a network. RDMA allows the GPU servers to directly access memory on the storage arrays, bypassing the CPU and reducing overhead. iSCSI and FCoE have higher latency due to the TCP/IP overhead. Gigabit Ethernet is far too slow. Standard TCP/IP over 100GbE is better than IOGbE iSCSI, but NVMe-oF with RDMA provides a significant performance advantage.
NEW QUESTION # 86
You are using BCM to manage a large cluster of GPU servers. You want to implement a mechanism to automatically scale the number of BCM instances based on the load. What Kubernetes feature would be MOST suitable for this purpose?
Answer: D
Explanation:
The Horizontal Pod Autoscaler (HPA) is the most suitable Kubernetes feature for automatically scaling the number of BCM instances (pods) based on resource utilization (e.g., CPU, memory). HPA monitors the resource usage of the BCM pods and automatically adjusts the number of replicas to maintain the desired resource levels. VPA adjusts the resource requests and limits of individual pods. Cluster Autoscaler adds or removes nodes from the cluster. Node Auto-Provisioning is related to node management. Kube-scheduler schedules pods onto nodes.
NEW QUESTION # 87
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?
Answer: E
Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.
NEW QUESTION # 88
A distributed training application using CUDA-Aware MPI and GPUDirect RDMA is experiencing performance degradation over time. You've ruled out network congestion and GPU utilization issues. What are TWO potential causes related to memory management that you should investigate?
Answer: B,D
Explanation:
GPU memory fragmentation can lead to smaller and smaller contiguous blocks of memory, making it difficult to allocate larger buffers needed for training, degrading performance over time. CUDA context switching overhead, if not managed correctly, can also significantly impact performance, especially in distributed environments where frequent communication and data transfers occur. CPU pinning affects process scheduling but doesn't directly cause performance degradation over time related to memory. Insufficient system RAM would likely cause more immediate errors or swapping. Improper use of 'MPI_Barrier' affects synchronization, not memory management specifically.
NEW QUESTION # 89
You are tasked with optimizing a BCM pipeline that processes video streams in real-time. The pipeline frequently misses frames, resulting in dropped video. What are the most effective strategies to reduce frame drops?
Answer: D
Explanation:
Reducing input complexity, optimizing computationally expensive steps, enabling parallelism, and increasing buffer sizes all help alleviate frame drops in real-time video processing pipelines.
NEW QUESTION # 90
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