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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability - Manage health, logs, and events - Troubleshoot network, compute, storage, and orchestration issues |
| Topic 2: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts |
| Topic 3: 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 - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
| Topic 4: AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
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NEW QUESTION # 37
A Cisco UCS C885A M8 server contains a GPU sled configured with six power supply units (PSUs) in an N+2 redundancy configuration. The workload on the GPU sled is currently drawing a load of 4500W. Using Power Save Mode, how many PSUs can be placed into standby mode to improve power efficiency without compromising redundancy?
Answer: B
Explanation:
With six PSUs in an N+2 configuration and a 4500W load, at least two PSUs must remain available to carry the active workload, and two additional PSUs must remain available to preserve redundancy. Therefore, four PSUs must stay active, allowing two PSUs to be placed into standby mode for power efficiency without compromising the N+2 redundancy requirement.
NEW QUESTION # 38
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?
Answer: C
Explanation:
Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.
NEW QUESTION # 39
A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?
Answer: B
Explanation:
Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.
NEW QUESTION # 40
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
Answer: D
Explanation:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.
NEW QUESTION # 41
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 # 42
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