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Cisco 300-640 Exam Syllabus Topics:

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

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Hot 300-640 Exam Labs | Well-Prepared 300-640 Best Preparation Materials: Implementing Cisco Data Center AI Infrastructure

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q36-Q41):

NEW QUESTION # 36
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?

A. UCS C845A M8 with AMD MI210 GPU
B. UCS C220 M8 with NVIDIA A16 GPU
C. UCS C885A M8 with NVIDIA H200 GPU
D. UCS C240 M8 with NVIDIA L40 GPU

Answer: C

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 # 37
A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?

A. Port mirroring
B. FIFO
C. MAC filtering
D. Weighted scheduling

Answer: D

Explanation:
Weighted scheduling allocates bandwidth according to configured priorities, ensuring critical AI traffic receives adequate forwarding resources during congestion. FIFO provides no prioritization, while MAC filtering and port mirroring are unrelated to congestion management or QoS scheduling.

NEW QUESTION # 38
An engineer must optimize the performance of an AI inference workload running on a Cisco UCS C-Series rack server. The workload experiences intermittent latency spikes, and Intersight system logs show frequent thermal event entries. Which troubleshooting action must be taken first to address the performance issues on the Cisco UCS server?

A. Check the power supply status and power consumption of the UCS server using UCS Manager to ensure that the server has sufficient power.
B. Review the resource utilization of the AI workload (CPU, memory, and GPU) to identify potential bottlenecks and optimize resource allocation.
C. Inspect the physical environment of the server, including ambient temperature and airflow, and replace any failed fans.
D. Examine the storage configuration, including the RAID configuration and disk health, to ensure that the storage subsystem is not causing latency issues.

Answer: C

Explanation:
Frequent thermal events indicate that the server may be throttling CPU or GPU performance to protect hardware from overheating. The first action is to verify the physical cooling conditions, including ambient temperature, airflow, and fan health, because resolving thermal constraints directly addresses the likely cause of intermittent latency spikes.

NEW QUESTION # 39
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?

A. Cisco UCS C240 Servers
B. Cisco UCS C845A M8 Server
C. Cisco UCS X-Series Direct
D. Cisco Unified Edge

Answer: D

Explanation:
Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.

NEW QUESTION # 40
A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

A. to prevent IP fragmentation that would break the InfiniBand sequence numbering and ordering guarantees
B. to indicate to intermediate switches that the packet contains RDMA traffic requiring special handling
C. to ensure that packets always take the same path through ECMP routing for consistent latency
D. to enable Path MTU Discovery so that the optimal packet size can be negotiated

Answer: A

Explanation:
RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.

NEW QUESTION # 41
......

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