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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate hybrid and multi-cloud AI integration - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| Topic 2: AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - 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 |
| Topic 3: AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability |
| Topic 4: AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
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NEW QUESTION # 75
How does RAG enhance the capabilities of LLMs?
Answer: A
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 # 76
A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?
Answer: C
Explanation:
Priority Flow Control (PFC) allows selective pausing of traffic classes during congestion, which is critical for RoCEv2 environments carrying GPU-to-GPU communication. AI workloads are highly sensitive to packet loss and retransmissions. BPDU Guard and DHCP Snooping are security features, while Storm Control only limits broadcast or multicast traffic and does not create lossless behavior for RDMA traffic.
NEW QUESTION # 77
Which components are part of the HyperFabric AI solution?
Answer: A
Explanation:
HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.
NEW QUESTION # 78
A network engineer must secure management access to Cisco Nexus AI switches. Which protocol should replace Telnet to provide encrypted administrative sessions?
Answer: B
Explanation:
SSH provides encrypted remote management sessions and protects credentials and configuration traffic from interception. Telnet transmits data in plaintext and is unsuitable for secure AI data center environments. FTP and TFTP are file transfer protocols, while SNMPv1 lacks modern security protections.
NEW QUESTION # 79
A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?
Answer: B
Explanation:
Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.
NEW QUESTION # 80
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P.S. Free 2026 Cisco 300-640 dumps are available on Google Drive shared by Prep4SureReview: https://drive.google.com/open?id=16SMwwqK7xeV_GSXeFrxT77Fu6aS7jMY3