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NVIDIA NCP-AIN Exam Syllabus Topics:

Topic	Details
Topic 1	AI Network Architecture: This section of the exam measures the skills of AI Infrastructure Architects and covers the ability to distinguish between AI factory and AI data center architectures. It includes understanding how Ethernet and InfiniBand differ in performance and application, and identifying the right storage options based on speed, scalability, and cost to fit AI networking needs.
Topic 2	Spectrum-X Configuration, Optimization, Security, and Troubleshooting: This section of the exam measures the skills of Network Performance Engineers and covers configuring, managing, and securing NVIDIA Spectrum-X switches. It includes setting performance baselines, resolving performance issues, and using diagnostic tools such as CloudAI benchmark, NCCL, and NetQ. It also emphasizes leveraging DPUs for network acceleration and using monitoring tools like Grafana and SNMP for telemetry analysis.
Topic 3	InfiniBand Configuration, Optimization, Security, and Troubleshooting: This section of the exam measures the skills of Data Center Network Administrators and covers the configuration and operational maintenance of NVIDIA InfiniBand switches. It includes setting up InfiniBand fabrics for multi-tenant environments, managing subnet configurations, testing connectivity, and using UFM to troubleshoot and analyze issues. It also focuses on validating rail-optimized topologies for optimal network performance.

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NVIDIA-Certified Professional AI Networking Sample Questions (Q86-Q91):

NEW QUESTION # 86
In Cumulus Linux, which technology enables the ability to provide active-active redundancy to servers, without the need for direct inter-switch links?

A. VSS
B. EVPN Multi-homing
C. MLAG

Answer: B

Explanation:
EVPN Multi-homingenablesactive-active redundancy without inter-switch links by using overlay routing over VXLAN and distributed control plane using BGP EVPN.

NEW QUESTION # 87
What command sequence is used to identify the exact name of the server that runs as the master SM in a multi-node fabric?

A. ibis
ibsim <LID>
B. sminfo
smpquery Nl <LID>
C. ibstat
sminfo <LID>
D. sminfo
smpquery ND <LID>

Answer: D

Explanation:
To identify the activeSubnet Manager (SM)node in an InfiniBand fabric, the correct command sequence is:
sminfo
Displays general information about the active SM in the fabric, including itsLID.
smpquery ND <LID>
Resolves theNode Description (ND)at the given LID, revealing the exact hostname or label of the SM server.

NEW QUESTION # 88
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. AI workloads have very high entropy that helps spread traffic evenly without congestion.
D. ECMP-based load balancing works best for AI workloads.

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 # 89
NVIDIA's AI networking solutions are most commonly used in which of the following environments?

A. Large-scale data centers
B. Home routers
C. Consumer gaming PCs
D. Mobile devices

Answer: A

Explanation:
NVIDIA's AI networking solutions are designed for data centers that support heavy AI/ML workloads, providing high-performance computing and networking infrastructure needed to train and deploy deep learning models.

NEW QUESTION # 90
Which of the following options correctly describes the difference between UFM Telemetry, UFM Enterprise, and UFM Cyber AI?

A. UFM Telemetry provides real-time monitoring and analysis of network performance. UFM Enterprise detects and mitigates network security threats, and UFM Cyber AI focuses on network management and optimization.
B. UFM Telemetry detects and mitigates network security threats. UFM Enterprise provides real-time monitoring and analysis of network performance, and UFM Cyber AI focuses on network management and optimization.
C. UFM Telemetry focuses on network management and optimization, UFM Enterprise detects and mitigates network security threats, and UFM Cyber AI provides real-time monitoring and analysis of network performance.
D. UFM Telemetry provides real-time monitoring and analysis of network performance, UFM Enterprise focuses on network management and optimization, and UFM Cyber AI detects and mitigates network security threats.

Answer: D

Explanation:
UFM Telemetry: Provides real-time monitoring and analysis of network performance, collecting data such as port counters and cable information to assess the health and efficiency of the network. UFM Enterprise: Focuses on comprehensive network management and optimization, enabling administrators to monitor, operate, and optimize InfiniBand scale-out computing environments effectively. UFM Cyber AI: Detects and mitigates network security threats by analyzing telemetry data to identify anomalies and potential security issues within the network infrastructure.

NEW QUESTION # 91
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