Pass Guaranteed Quiz 2026 Valid Cisco Latest 300-640 Exam Tips

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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q42-Q47):

NEW QUESTION # 42
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.

Answer:

Explanation:

NEW QUESTION # 43
What does workload distribution offer in an AI infrastructure with local and external resources?

A. isolation of analytic functions within remote processing clusters
B. enforcement of fixed security policies on distribution endpoints
C. restriction of automation to local storage and performance measures
D. distribution of computational tasks across multiple locations

Answer: C

Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.

NEW QUESTION # 44
Which components are part of the HyperFabric AI solution?

A. integrated compute and networking hardware optimized for AI workloads, with optional cloud management
B. combination of Cisco networking hardware and software-defined storage designed for AI scalability
C. Cisco UCS servers combined with high-speed networking switches and third-party storage
D. Cisco UCS servers with NVIDIA GPUs, switches, optional VAST Data Storage and cloud- managed networking

Answer: D

Explanation:
HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.

NEW QUESTION # 45
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

A. distinct management for isolated components
B. limited connectivity within isolated locations
C. efficient launch of computational platforms
D. delayed rollout of new network resources

Answer: C

Explanation:
Rapid provisioning supports scalable AI workload execution by enabling computational platforms to be launched quickly and consistently as workload demand grows. This allows AI infrastructure to add or prepare compute resources efficiently for training, inference, or data-processing tasks.

NEW QUESTION # 46
What is a function of NVLink technology within a high-performance AI server or cluster?

A. to enable remote access and virtualization of GPU resources for distributed AI development teams
B. to provide a high-speed, low-latency direct interconnect for GPU-to-GPU communication
C. to manage power distribution and thermal regulation across all installed GPU units
D. to establish a secure, encrypted channel for data transfer between the AI server and external storage

Answer: B

Explanation:
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.

NEW QUESTION # 47
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