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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Infrastructure Fundamentals20%- Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring
- Describe AI use cases in data center environments
- Describe types of AI infrastructure: on-premises, cloud, hybrid, edge
- Describe AI and machine learning concepts
- Explain Cisco AI infrastructure solutions and portfolio
Topic 2: AI Infrastructure Operations and Troubleshooting20%- Manage health, logs, and events
- Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
- Troubleshoot network, compute, storage, and orchestration issues
- Optimize performance, security, and reliability
- Implement performance benchmarking and validation
Topic 3: AI Infrastructure Deployment and Data Management30%- Configure compute and storage using Cisco UCS: profiles, policies, Intersight
- Deploy and manage AI infrastructure with orchestration tools
- 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
Topic 4: AI Infrastructure Components and Architecture30%- Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures
- Evaluate hybrid and multi-cloud AI integration
- 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

>> Latest 300-640 Test Question <<

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q69-Q74):

NEW QUESTION # 69
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?

Answer: A

Explanation:
Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.


NEW QUESTION # 70
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: D

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 # 71
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

Answer: C

Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


NEW QUESTION # 72
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?

Answer: D

Explanation:
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.


NEW QUESTION # 73
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?

Answer: D

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 # 74
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