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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Manage health, logs, and events - 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 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 - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures |
| AI Infrastructure Fundamentals | 20% | - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
| AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools |
The Cisco 300-640 certification verifies that you have a basic understanding of Implementing Cisco Data Center AI Infrastructure concepts and virtualization. Success in the 300-640 exam of the Cisco 300-640 certificate also proves your knowledge of basic troubleshooting concepts and data center technology. When you earn the 300-640 Certification you will get reliable exam guide materials.
NEW QUESTION # 62
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?
Answer: A
Explanation:
RoCEv2 encapsulates RDMA traffic over UDP/IP, enabling routable RDMA communication across Layer 3 networks. TCP introduces retransmission and latency overhead unsuitable for high-performance GPU communication. SCTP and ICMP are not used for RoCEv2 transport.
NEW QUESTION # 63
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?
Answer: C
Explanation:
Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.
NEW QUESTION # 64
Drag and Drop Question
An organization is deploying an AI/ML infrastructure with multiple network fabrics, each serving a specific purpose. The network architect must match the network fabric type with its primary function. Drag and drop the network fabric types from the left onto the corresponding descriptions on the right.
Answer:
Explanation:
NEW QUESTION # 65
A data center facility experienced a power outage, and the UCS X-Series servers did not restart automatically when power is restored. The configuration must be updated to prioritize these servers to boot as soon as possible to resume their work. Which set of power policy configurations must be used to meet these requirements?
Answer: C
Explanation:
The servers must be configured to power back on automatically after power is restored, so the restore behavior should be set to always power on. Setting the power priority to high ensures these UCS X-Series servers receive priority during power recovery, allowing them to resume workloads as quickly as possible.
NEW QUESTION # 66
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 # 67
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