NVIDIA NCP-AIN Exam | Latest NCP-AIN Exam Online - Help you Pass NCP-AIN Free Test Questions Once
NVIDIA NCP-AIN Exam | Latest NCP-AIN Exam Online - Help you Pass NCP-AIN Free Test Questions Once
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NVIDIA NCP-AIN Exam Syllabus Topics:
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NVIDIA-Certified Professional AI Networking Sample Questions (Q18-Q23):
NEW QUESTION # 18
What is the total throughput of the SN5600 Spectrum-X switch?
- A. 25.6 terabits per second
- B. 12.8 petabits per second
- C. 102.4 gigabits per second
- D. 51.2 terabits per second
Answer: D
Explanation:
The SN5600 smart-leaf/spine/super-spine switch offers 64 ports of 800GbE in a dense 2U form factor. The SN5600 offers diverse connectivity in combinations of 1 to 800GbE and boasts an industry-leading total throughput of 51.2Tb/s.
Reference:NVIDIA Spectrum SN5600 Ethernet Switch - Bluum
NEW QUESTION # 19
You are optimizing an AI workload that involves multiple GPUs across different nodes in a data center. The application requires both high-bandwidth GPU-to-GPU communication within nodes and efficient communication between nodes.
Which combination of NVIDIA technologies would best support this multi-node, multi-GPU AI workload?
- A. PCIe for intra-node GPU communication and RoCE for inter-node communication.
- B. NVLink for both intra-node and inter-node GPU communication.
- C. InfiniBand for both intra-node and inter-node GPU communication.
- D. NVLink for intra-node GPU communication and InfiniBand for inter-node communication.
Answer: D
Explanation:
For optimal performance in multi-node, multi-GPU AI workloads:
* NVLinkprovides high-speed, low-latency communication between GPUs within the same node.
* InfiniBandoffers efficient, scalable communication between nodes in a data center.Combining these technologies ensures both intra-node and inter-node communication needs are effectively met.
Reference:NVIDIA NVLink & NVSwitch: Fastest HPC Data Center Platform
NEW QUESTION # 20
In an AI cluster using NVIDIA GPUs, which configuration parameter in the NicClusterPolicy custom resource is crucial for enabling high-speed GPU-to-GPU communication across nodes?
- A. OFED Driver
- B. Secondary Network
- C. NV IPAM
- D. RDMA Shared Device Plugin
Answer: D
Explanation:
The RDMA Shared Device Plugin is a critical component in the NicClusterPolicy custom resource for enabling Remote Direct Memory Access (RDMA) capabilities in Kubernetes clusters. RDMA allows for high- throughput, low-latency networking, which is essential for efficient GPU-to-GPU communication across nodes in AI workloads. By deploying the RDMA Shared Device Plugin, the cluster can leverage RDMA- enabled network interfaces, facilitating direct memory access between GPUs without involving the CPU, thus optimizing performance.
Reference Extracts from NVIDIA Documentation:
* "RDMA Shared Device Plugin: Deploy RDMA Shared device plugin. This plugin enables RDMA capabilities in the Kubernetes cluster, allowing high-speed GPU-to-GPU communication across nodes."
* "The RDMA Shared Device Plugin is responsible for advertising RDMA-capable network interfaces to Kubernetes, enabling pods to utilize RDMA for high-performance networking."
NEW QUESTION # 21
You are concerned about potential security threats and unexpected downtime in your InfiniBand data center.
Which UFM platform uses analytics to detect security threats, operational issues, and predict network failures in InfiniBand data centers?
- A. Host Agent
- B. Cyber-AI Platform
- C. Telemetry Platform
- D. Enterprise Platform
Answer: B
Explanation:
TheNVIDIA UFM Cyber-AI Platformis specifically designed to enhance security and operational efficiency in InfiniBand data centers. It leverages AI-powered analytics to detect security threats, operational anomalies, and predict potential network failures. By analyzing real-time telemetry data, it identifies abnormal behaviors and performance degradation, enabling proactive maintenance and threat mitigation.
This platform integrates with existing UFM Enterprise and Telemetry services to provide a comprehensive view of the network's health and security posture. It utilizes machine learning algorithms to establish baselines for normal operations and detect deviations that may indicate security breaches or hardware issues.
Reference:NVIDIA UFM Cyber-AI Documentation v2.9.1
NEW QUESTION # 22
What is the purpose of configuring NVUE to ignore Linux files?
- A. Reduce NVUE memory utilization to optimize performance.
- B. Enable pushing of configuration through Ansible template files.
- C. Enable the persistent manipulation of specific settings using both NVUE and flat-file approaches.
- D. Improve Cumulus security by reducing the attack surface.
Answer: C
Explanation:
Configuring NVUE to ignore certain underlying Linux files allows administrators to manage specific settings manually or through automation tools like Ansible withoutNVUE overwriting these configurations. This approach enables the persistent manipulation of settings using both NVUE and flat-file methods, providing flexibility in network management.
NEW QUESTION # 23
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