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Test NCP-AIN Cram | NCP-AIN Visual Cert Test

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NVIDIA-Certified Professional AI Networking Sample Questions (Q24-Q29):

NEW QUESTION # 24
A user has requested confirmation that the InfiniBand network is performing optimally and is not limiting the speed of a training run. To verify this, you would like to measure the RDMA throughput rate between two endpoints.
Which tool should be used?

A. ib_write_bw
B. iperf
C. ping
D. ibdiagnet

Answer: A

Explanation:
The ib_write_bw tool is part of the Perftest package and is specifically designed to measure the bandwidth of RDMA write operations between two InfiniBand endpoints. It provides accurate assessments of RDMA throughput, which is crucial for verifying the performance of InfiniBand networks in high-performance computing and AI training environments.
Reference:ib_write_bw - NVIDIA Enterprise Support Portal

NEW QUESTION # 25
How does Spectrum-X achieve network isolation for multiple tenants?

A. Using manual configuration of access control lists (ACLs).
B. By implementing physical network segmentation.
C. By assigning unique IP address ranges to each tenant.
D. By implementing a Layer 3 Virtual Network Identifier (L3VNI) per VRR

Answer: D

Explanation:
Spectrum-X achieves network isolation in multi-tenant environments by implementing Layer 3 Virtual Network Identifiers (L3VNIs) per Virtual Routing and Forwarding (VRF) instance. This approach allows each tenant to have a separate routing table and network segment, ensuring that traffic is isolated and secure between tenants.
Reference Extracts from NVIDIA Documentation:
* "Spectrum-X enhances multi-tenancy with performance isolation to ensure tenants' AI workloads perform optimally and consistently."

NEW QUESTION # 26
What are the prerequisites for performing Flow Analysis with NetQ?

A. Cumulus 5.x and later / Spectrum-3 and later / On-premises deployment
B. Cumulus 4.x and later / Spectrum-2 and later / LCM enabled
C. Cumulus 5.x and later / Spectrum-2 and later / LCM enabled
D. Cumulus 5.x and later / Spectrum-2 and later / On-premises deployment

Answer: C

Explanation:
To perform Flow Analysis with NetQ, the following prerequisites must be met:
* Cumulus Linux Version: NetQ Flow Analysis requires Cumulus Linux 5.x or later.
* Switch Hardware: The feature is supported on Spectrum-2 and later switch models.
* Lifecycle Management (LCM): LCM must be enabled to utilize Flow Analysis capabilities.
These requirements ensure compatibility and proper functioning of the Flow Analysis feature within NetQ.
Reference: NVIDIA NetQ Documentation - Flow Analysis Prerequisites

NEW QUESTION # 27
You are troubleshooting InfiniBand connectivity issues in a cluster managed by the NVIDIA Network Operator. You need to verify the status of the InfiniBand interfaces. Which command should you use to check the state and link layer of InfiniBand interfaces on a node?

A. rdma show devices
B. ip link show dev ib0
C. ifconfig ib0
D. ibstat -d mlx5_X

Answer: D

Explanation:
To check the status and link layer of InfiniBand interfaces, the ibstat command is used. For example:
ibstat -d mlx5_0
This command provides detailed information about the InfiniBand device, including its state (e.g., Active), physical state (e.g., LinkUp), and link layer (e.g., InfiniBand).
Reference: NVIDIA DGX BasePOD Deployment Guide - Network Operator Section

NEW QUESTION # 28
Which of the following statements are true about AI workloads and adaptive routing?
Pick the 2 correct responses below.

A. Flow-based load balancing mechanisms increase congestion risk.
B. AI workloads are made of a small number of volumetric flows called elephant flows.
C. ECMP-based load balancing works best for AI workloads.
D. AI workloads have very high entropy that helps spread traffic evenly without congestion.

Answer: A,B

Explanation:
AI workloads, particularly in large-scale training scenarios, are characterized by a small number of high- bandwidth, long-lived flows known as "elephant flows." These flows can dominate network traffic and are prone to causing congestion if not managed effectively.
Traditional flow-based load balancing mechanisms, such as Equal-Cost Multipath (ECMP), distribute traffic based on flow hashes. However, in AI workloads with lowentropy (i.e., limited variability in flow characteristics), ECMP can lead to uneven traffic distribution and congestion on certain paths.
Adaptive routing techniques, which dynamically adjust paths based on real-time network conditions, are more effective in managing AI traffic patterns and mitigating congestion risks.
Reference:Powering Next-Generation AI Networking with NVIDIA SuperNICs

NEW QUESTION # 29
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