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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
| AI Infrastructure Operations and Troubleshooting | 20% | - Manage health, logs, and events - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues |
| AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration |
| AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
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NEW QUESTION # 81
A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
- policy managed through cloud-based interface
- low latency between specific pairs of GPUs
- at least 2 GPUs per server
- all GPUs should be same model
- Intel CPU
- well suited for Inferencing
- customer is power conscious
Which server meets the requirements?
Answer: D
Explanation:
The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
Reference:
https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf
NEW QUESTION # 82
An engineer configures Cisco Nexus Dashboard Fabric Controller for AI infrastructure automation. The company wants centralized policy management and simplified fabric provisioning across multiple pods. Which capability provides this benefit?
Answer: A
Explanation:
Intent-based fabric automation simplifies deployment and policy consistency across large AI infrastructures. Nexus Dashboard Fabric Controller automates provisioning, validation, and operational management. Distributed ACLs and local VLAN switching are operational features but do not centralize orchestration. Manual CLI verification is error-prone and unsuitable for rapidly scaling AI environments.
NEW QUESTION # 83
An engineer notices excessive broadcast traffic inside a legacy AI server VLAN. Which technology should be implemented to reduce flooding and improve scalability?
Answer: C
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 # 84
How does RAG enhance the capabilities of LLMs?
Answer: B
Explanation:
RAG enhances LLMs by retrieving relevant external or proprietary information at generation time and using that context to produce more accurate, current, and domain-specific responses without changing the model's core parameters.
NEW QUESTION # 85
An engineer must configure dynamic load balancing (DLB) on a Cisco Nexus 9000 Series Switch AI fabric using a CLI. Which configuration must be implemented for only the odd-numbered interfaces?
Answer: C
Explanation:
Dynamic load balancing is enabled on the selected Cisco Nexus 9000 interfaces by applying the interface-level dlb-interface command. For this deployment requirement, the command is applied only to the odd-numbered interfaces that participate in the DLB configuration.
NEW QUESTION # 86
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