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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools |
| AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge |
| AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues |
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NEW QUESTION # 25
Drag and Drop Question
Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.
Answer:
Explanation:
NEW QUESTION # 26
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 # 27
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?
Answer: A
Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.
NEW QUESTION # 28
What is a function of NVLink technology within a high-performance AI server or cluster?
Answer: C
Explanation:
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.
NEW QUESTION # 29
A national law firm is deploying a RAG solution for their legal research team to more efficiently use their 50 TB of confidential data and documents when preparing new contracts and briefs.
They decided on a UCS X-Series chassis equipped with four GPUs and six X210c servers. Which storage solution fits this deployment?
Answer: D
Explanation:
A RAG deployment with 50 TB of confidential documents requires scalable shared storage that can be accessed by multiple UCS X210c servers. An IP-based storage array provides centralized capacity, shared access, and operational simplicity for storing and retrieving large datasets used by the RAG pipeline.
NEW QUESTION # 30
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