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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions |
| Topic 2: AI Infrastructure Operations | 20% | - Troubleshooting Common Issues - Performance Optimization - Monitoring and Management Tools - Capacity Planning |
| Topic 3: AI Infrastructure Deployment | 25% | - Cabling and Connectivity - Storage Configuration for AI - Hardware Installation and Configuration - Network Switch Configuration - GPU Server Configuration |
| Topic 4: AI Infrastructure Security and Compliance | 10% | - Security Best Practices - Compliance Considerations - Access Control and Authentication |
| Topic 5: AI Infrastructure Fundamentals | 15% | - AI Workload Characteristics - AI Ecosystem Components - GPU Architecture and Types - AI Infrastructure Requirements |
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NEW QUESTION # 13
A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?
Answer: C
Explanation:
VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.
NEW QUESTION # 14
An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?
Answer: B
Explanation:
BGP EVPN provides scalable control-plane MAC and IP route advertisement for VXLAN fabrics.
It eliminates excessive flooding associated with traditional Layer 2 networks and supports efficient multi-tenant AI infrastructures. STP only prevents loops, OSPF is a Layer 3 routing protocol, and HSRP provides gateway redundancy rather than overlay endpoint learning.
NEW QUESTION # 15
An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?
Answer: A
Explanation:
Traffic Analytics and Flow Telemetry use overlapping telemetry resources in Cisco Nexus Dashboard. When Traffic Analytics is enabled to monitor congestion, latency, and drops, Flow Telemetry is automatically disabled to avoid resource and data collection conflicts.
NEW QUESTION # 16
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?
Answer: B
Explanation:
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.
NEW QUESTION # 17
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?
Answer: B
Explanation:
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.
NEW QUESTION # 18
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