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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
AI Infrastructure Fundamentals	15%	- AI Infrastructure Requirements - GPU Architecture and Types - AI Workload Characteristics - AI Ecosystem Components
Cisco AI Infrastructure Architecture	30%	- Storage Architecture Considerations - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions
AI Infrastructure Operations	20%	- Monitoring and Management Tools - Troubleshooting Common Issues - Performance Optimization - Capacity Planning
AI Infrastructure Deployment	25%	- Network Switch Configuration - Hardware Installation and Configuration - GPU Server Configuration - Storage Configuration for AI - Cabling and Connectivity
AI Infrastructure Security and Compliance	10%	- Compliance Considerations - Access Control and Authentication - Security Best Practices

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q31-Q36):

NEW QUESTION # 31
What is a purpose of Cisco AI PODs?

A. to enable AI lifecycle management through cloud-native software with hardware support limited to compute servers
B. to offer a flexible AI infrastructure focused on inferencing workloads with optional training capabilities
C. to provide a turnkey, full-stack infrastructure for AI training, fine-tuning, and inferencing
D. to deliver modular AI hardware components that can be integrated with third-party software platforms

Answer: C

Explanation:
Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.

NEW QUESTION # 32
An Intersight administrator plans to deploy new server solutions to several small branch offices across the country. Each location needs at least one chassis. The servers must have redundant CPUs, memory, and 200 Gbps of unified fabric connectivity per compute node. Which hybrid AI compute solution meets the requirements?

A. Cisco UCS X-Direct
B. Cisco UCS C885A M8
C. Cisco UCS S3260
D. Cisco UCSXE Unified Edge

Answer: A

Explanation:
Cisco UCS X-Direct is designed for smaller distributed or branch deployments that still need chassis-based UCS X-Series compute capabilities. It supports redundant compute resources and high-speed unified fabric connectivity, including 200 Gbps per compute node, making it the best fit for hybrid AI workloads across multiple branch office locations.

NEW QUESTION # 33
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?

A. Use a single Cisco Fabric Interconnect in a standalone configuration.
Connect all UCS servers directly to it using 10 Gb Ethernet.
B. Implement a dual-fabric design with two Cisco Fabric Interconnects, and use 100 Gb Ethernet for server-to-fabric connectivity.
Configuring port channels for uplink connections.
C. Deploy a Cisco MDS 9000 Series Switch for Fibre Channel connectivity to storage.
Use a Cisco Fabric Interconnect for management traffic only.
D. Connect UCS servers to a Cisco Nexus 3000 Series Switch using 100 Gb Ethernet.
Configure a separate management network using a dedicated Cisco Catalyst switch.

Answer: B

Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.

NEW QUESTION # 34
Which function is provided through natural language processing?

A. automated conversion of spoken or written content into structured data
B. filtration of duplicate entries from transaction records
C. compression of large configuration files for storage efficiency
D. annotation of data transmission protocols for device authorization

Answer: A

Explanation:
Natural language processing enables systems to interpret, process, and transform human language from spoken or written inputs into structured data that applications can analyze, search, classify, or use for automation.

NEW QUESTION # 35
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?

A. Configure weighted fair queuing to prioritize RDMA traffic over other traffic classes.
B. Enable Priority Flow Control on the traffic class carrying RoCEv2 packets.
C. Set the RDMA traffic to use the highest DSCP value to ensure priority forwarding.
D. Implement traffic shaping to smooth RDMA traffic bursts and prevent congestion.

Answer: B

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 # 36
......

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