300-640 Demotesten & 300-640 Prüfungsvorbereitung

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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Infrastructure Operations and Troubleshooting20%- Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
- Troubleshoot network, compute, storage, and orchestration issues
- Optimize performance, security, and reliability
- Implement performance benchmarking and validation
- Manage health, logs, and events
Topic 2: AI Infrastructure Components and Architecture30%- Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability
- Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures
- Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS
- Evaluate power, cooling, and efficiency requirements
- Evaluate hybrid and multi-cloud AI integration
Topic 3: AI Infrastructure Deployment and Data Management30%- Implement data management and lifecycle for AI workloads
- Configure compute and storage using Cisco UCS: profiles, policies, Intersight
- Integrate AI frameworks and applications with Cisco infrastructure
- Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics
- Deploy and manage AI infrastructure with orchestration tools
Topic 4: AI Infrastructure Fundamentals20%- Describe AI and machine learning concepts
- 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 use cases in data center environments

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300-640 Übungstest: Implementing Cisco Data Center AI Infrastructure & 300-640 Braindumps Prüfung

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Cisco Implementing Cisco Data Center AI Infrastructure 300-640 Prüfungsfragen mit Lösungen (Q64-Q69):

64. Frage
An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?

Antwort: B

Begründung:
RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.


65. Frage
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?

Antwort: A

Begründung:
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.


66. Frage
Drag and Drop Question

Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.

Antwort:

Begründung:


67. Frage
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

Antwort: B

Begründung:
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.


68. Frage
Drag and Drop Question
A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.

Antwort:

Begründung:


69. Frage
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