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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
AI Infrastructure Operations20%- Monitoring and Management Tools
- Troubleshooting Common Issues
- Capacity Planning
- Performance Optimization
Cisco AI Infrastructure Architecture30%- Hyperconverged Infrastructure for AI
- Network Architecture for AI Workloads
- Storage Architecture Considerations
- Cisco UCS AI Infrastructure Solutions
AI Infrastructure Fundamentals15%- AI Infrastructure Requirements
- GPU Architecture and Types
- AI Workload Characteristics
- AI Ecosystem Components
AI Infrastructure Deployment25%- Hardware Installation and Configuration
- Cabling and Connectivity
- Network Switch Configuration
- GPU Server Configuration
- Storage Configuration for AI
AI Infrastructure Security and Compliance10%- Compliance Considerations
- Access Control and Authentication
- Security Best Practices

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q65-Q70):

NEW QUESTION # 65
An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements:
- Support 256 GPU servers with RDMA connectivity.
- Prevent any packet loss that causes RDMA connection failures.
- Maintain consistent low-latency communication with a target of less
than 10 microseconds.
- Use industry-standard protocols and configurations.
Which configuration ensures that RoCEv2 operates as a lossless transport?

Answer: A

Explanation:
RoCEv2 requires lossless Ethernet behavior to prevent packet drops that can disrupt RDMA communication. Enabling Priority Flow Control on the traffic class carrying RoCEv2 packets allows Ethernet pause behavior for that priority, helping maintain reliable, low-latency RDMA transport across the AI training fabric.


NEW QUESTION # 66
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

Answer: B

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 # 67
An end customer plans to deploy an AI fabric in a new data center. Their engineers are more familiar with CLI-based networks and require the capability to automate standardized configuration templates and monitor flows for the fabric. Which data center automation tool meets the requirements?

Answer: C

Explanation:
Nexus Dashboard is the Cisco data center automation and operations platform used with Nexus fabrics. It supports standardized fabric configuration through automation templates and provides visibility features such as flow monitoring, making it the appropriate tool for engineers who need CLI-oriented data center fabric operations with automation and telemetry.


NEW QUESTION # 68
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)

Answer: A,B

Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.


NEW QUESTION # 69
A company is building a network fabric for an AI training cluster that will train large language models. The cluster includes 256 GPUs distributed across 32 servers. Security requirements mandate isolation between different training projects and allowing shared access to a central storage system. Which approach provides the required security isolation and maintains optimal performance for GPU-to-GPU communication?

Answer: A

Explanation:
VXLAN provides scalable network segmentation by assigning separate VNIs to different training projects, preserving isolation without building separate physical fabrics. Group Policy Option adds policy-based control within the VXLAN overlay, allowing controlled shared access to central storage while maintaining high-performance GPU-to-GPU communication across the AI training fabric.


NEW QUESTION # 70
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