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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment and Data Management | 30% | - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads |
| AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts - Describe AI use cases in data center environments |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
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NEW QUESTION # 43
An engineer notices excessive broadcast traffic inside a legacy AI server VLAN. Which technology should be implemented to reduce flooding and improve scalability?
Answer: A
Explanation:
VXLAN EVPN reduces unknown unicast and broadcast flooding by using control-plane learning with BGP EVPN. This improves scalability in modern AI data centers. RIP is outdated for large- scale fabrics, while PAT and GRE tunneling do not address broadcast domain limitations.
NEW QUESTION # 44
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: D
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 # 45
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?
Answer: D
Explanation:
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.
NEW QUESTION # 46
An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?
Answer: A
Explanation:
VAST storage integration in a Cisco ACI fabric requires QoS settings that support lossless, congestion-aware Ethernet behavior. Weighted Random Early Detection with congestion notification enabled allows the fabric to signal congestion early through ECN before buffer exhaustion occurs, helping maintain predictable performance for high-throughput storage traffic.
NEW QUESTION # 47
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)
Answer: C,E
Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.
NEW QUESTION # 48
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