MORE DETAILS ABOUT NVIDIA NCP-AIN EXAM DUMPS

More Details About NVIDIA NCP-AIN Exam Dumps

More Details About NVIDIA NCP-AIN Exam Dumps

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Authorized NCP-AIN Test Dumps - New NCP-AIN Test Bootcamp

This offline version of the practice test creates a real NVIDIA-Certified Professional AI Networking exam environment. You can practice the NVIDIA NCP-AIN Questions with the help of desktop practice exam software. The practice exam software is compatible with Windows-based computers only and does not need internet connectivity.

NVIDIA-Certified Professional AI Networking Sample Questions (Q61-Q66):

NEW QUESTION # 61
Why is the InfiniBand LRH called a local header?

  • A. It allows traffic on a local link only.
  • B. It is used for routing traffic between nodes in the local subnet.
  • C. It provides the LIDs from the local subnet manager.
  • D. It provides the parameters for each local HCA.

Answer: B

Explanation:
TheLocal Route Header (LRH)in InfiniBand is termed "local" because it is used exclusively for routing packets within a single subnet. The LRH contains the destination and source Local Identifiers (LIDs), which are unique within a subnet, facilitating efficient routing without the need for global addressing. This design optimizes performance and simplifies routing within localized network segments.
InfiniBand is a high-performance, low-latency interconnect technology widely used in AI and HPC data centers, supported by NVIDIA's Quantum InfiniBand switches and adapters. The Local Routing Header (LRH) is a critical component of the InfiniBand packet structure, used to facilitate routing within an InfiniBand fabric. The question asks why the LRH is called a "local header," which relates to its role in the InfiniBand network architecture.
According to NVIDIA's official InfiniBand documentation, the LRH is termed "'local' because it contains the addressing information necessary for routing packets between nodes within the same InfiniBand subnet." The LRH includes fields such as the Source Local Identifier (SLID) and Destination Local Identifier (DLID), which are assigned by the subnet manager to identify the source and destination endpoints within the local subnet. These identifiers enable switches to forward packets efficiently within the subnet without requiring global routing information, distinguishing the LRH from the Global Routing Header (GRH), which is used for inter-subnet routing.
Exact Extract from NVIDIA Documentation:
"The Local Routing Header (LRH) is used for routing InfiniBand packets within a single subnet. It contains the Source LID (SLID) and Destination LID (DLID), which are assigned by the subnet manager to identify the source and destination nodes in the local subnet. The LRH is called a 'local header' because it facilitates intra-subnet routing, enabling switches to forward packets based on LID-based forwarding tables."
-NVIDIA InfiniBand Architecture Guide
This extract confirms that option A is the correct answer, as the LRH's primary function is to route traffic between nodes within the local subnet, leveraging LID-based addressing. The term "local" reflects its scope, which is limited to a single InfiniBand subnet managed by a subnet manager.
Reference:LRH and GRH InfiniBand Headers - NVIDIA Enterprise Support Portal


NEW QUESTION # 62
You are deploying a Kubernetes cluster for AI workloads using NVIDIA Spectrum-X switches. You need to automate the deployment and management of networking components in this environment.
Which NVIDIA tool is specifically designed to automate the deployment and management of networking components in a Kubernetes cluster with Spectrum-X switches?

  • A. Mellanox OFED
  • B. Container Runtime
  • C. GPU Operator
  • D. Network Operator

Answer: D

Explanation:
TheNVIDIA Network Operatoris designed to simplify and automate the deployment and management of networking components in Kubernetes clusters, particularly those utilizing NVIDIA Spectrum-X switches. It manages the installation and configuration of necessary drivers, plugins, and other networking resources to enable features like RDMA and GPUDirect RDMA, which are essential for high-performance AI workloads.
By leveraging Kubernetes Custom Resource Definitions (CRDs) and the Operator Framework, the Network Operator ensures that networking components are consistently and correctly configured across the cluster, reducing manual intervention and potential configuration errors.
Reference:NVIDIA Network Operator Documentation


NEW QUESTION # 63
You need to configure a bond in Cumulus Linux. Which command should you use?

  • A. nv set bondbond1 interface member swp1-4
  • B. nv set interface bond1 bond mode lacp
  • C. nv set interface bond1 bond mlag enable
  • D. nv set interface bond1 bond member swp1-4

Answer: B

Explanation:
In Cumulus Linux, configuring a bond interface with Link Aggregation Control Protocol (LACP) involves setting the bond mode to 'lacp'. The correct command to achieve this is:
nv set interface bond1 bond mode lacp
This command sets the bonding mode of 'bond1' to LACP, enabling dynamic link aggregation for increased bandwidth and redundancy.
Reference Extracts from NVIDIA Documentation:
* "To reset the link aggregation mode for bond1 to the default value of 802.3ad, run the nv set interface bond1 bond mode lacp command."


NEW QUESTION # 64
You are planning to deploy a large-scale Spectrum-X network for AI workloads. Before physical implementation, you want to validate the network design and configuration using a digital twin approach.
Which NVIDIA tool would be most appropriate for creating and simulating a digital twin of your Spectrum-X network?

  • A. NVIDIA Omniverse
  • B. NVIDIA Base Command Manager
  • C. NVIDIA NetQ
  • D. NVIDIA Air

Answer: D

Explanation:
NVIDIA Air is a cloud-based network simulation tool designed to create digital twins of data center infrastructure, including Spectrum-X networks. It allows users to model switches, SuperNICs, and storage components, enabling the simulation, validation, and automation of network configurations before physical deployment. This facilitates Day 0, 1, and 2 operations, ensuring that network designs are tested and optimized for AI workloads.
Reference Extracts from NVIDIA Documentation:
* "NVIDIA Air enables cloud-scale efficiency by creating identical replicas of real-world data center infrastructure deployments."
* "NVIDIA Air allows users to model data center deployments with full software functionality, creating a digital twin. Transform and accelerate time to AI by simulating, validating, and automating changes and updates."
* "NVIDIA Air supports simulation of NVIDIA Spectrum Ethernet (Cumulus Linux and SONiC) switches and NVIDIA BlueField DPUs and SuperNICs as well as the NetQ network operations toolset."


NEW QUESTION # 65
A major cloud provider is designing a new data center to support large-scale AI workloads, particularly for training large language models. They want to optimize their network architecture for maximum performance and efficiency.
Why is a rail-optimized topology considered a best practice for AI network architecture in this scenario?

  • A. It maximizes the number of network hops to increase data redundancy.
  • B. It simplifies network management by using a single large switch for all connections.
  • C. It provides optimal GPU-to-GPU communication and reduces network interference between flows.
  • D. It prioritizes north-south traffic over east-west traffic for better internet connectivity.

Answer: C

Explanation:
A rail-optimized topology is designed to enhance GPU-to-GPU communication by connecting each GPU's Network Interface Card (NIC) to a dedicated rail switch. This configuration ensures predictable traffic patterns and minimizes network interference between data flows, which is crucial for the performance of large-scale AI workloads, such as training large language models. By reducing contention and latency, this topology supports efficient and scalable AI training environments.
Reference Extracts from NVIDIA Documentation:
* "Rail-optimized network topology helps maximize all-reduce performance while minimizing network interference between flows."
* "A Rail Optimized Stripe Architecture provides efficient data transfer between GPUs, especially during computationally intensive tasks such as AI Large Language Models (LLM) training workloads, where seamless data transfer is necessary to complete the tasks within a reasonable timeframe."


NEW QUESTION # 66
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