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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q59-Q64):

NEW QUESTION # 59
A network architect designs connectivity for a new Cisco AI POD operating in Intersight Managed Mode. To ensure high availability and aggregate bandwidth for external network traffic, a port channel must be established using multiple 100 GbE uplink ports on the fabric interconnects to connect to the upstream core switch infrastructure. Which specific policy, applied through the domain profile, must be modified to define and configure this port channel on the fabric interconnects for the desired uplink connectivity?

Answer: B

Explanation:
In Cisco Intersight Managed Mode, fabric interconnect port roles and uplink port channels are defined in the port policy associated with the UCS domain profile. Modifying the port policy allows the engineer to configure multiple 100 GbE uplink ports as a port channel for aggregated bandwidth and high availability toward the upstream network.


NEW QUESTION # 60
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: B,E

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 # 61
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 # 62
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 # 63
An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?

Answer: A

Explanation:
VAST Data Universal Storage provides a software-defined, disaggregated shared-everything storage architecture designed for AI-scale workloads. It supports Cisco UCS-based deployments managed through Intersight and delivers high-performance all-flash storage with RDMA and GPU Direct Storage capabilities for large-scale AI training, fine-tuning, and inference environments.


NEW QUESTION # 64
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