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Cisco 300-640 Exam Syllabus Topics:

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

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Free PDF 2026 Cisco 300-640: Marvelous Implementing Cisco Data Center AI Infrastructure Best Study Material

Our to-the-point and trustworthy Cisco 300-640 Exam Questions in three formats for the Cisco 300-640 certification exam will surely assist you to qualify for Implementing Cisco Data Center AI Infrastructure certification. Do not underestimate the value of our Cisco 300-640 Exam Dumps because it is the make-or-break point of your career.

Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q67-Q72):

NEW QUESTION # 67
A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
- policy managed through cloud-based interface
- low latency between specific pairs of GPUs
- at least 2 GPUs per server
- all GPUs should be same model
- Intel CPU
- well suited for Inferencing
- customer is power conscious
Which server meets the requirements?

A. C845A M8
B. C245 M8
C. C885A M8
D. X210C M8 with x580 PCIe node

Answer: D

Explanation:
The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
Reference:
https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf

NEW QUESTION # 68
What describes inference traffic patterns in AI deployments?

A. simplifies complex neural network architectures
B. eliminates the need for data preparation
C. provides real-time decision making
D. optimizes data storage solutions for data lakes

Answer: C

Explanation:
Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.

NEW QUESTION # 69
Drag and Drop Question
An organization is deploying an AI/ML infrastructure with multiple network fabrics, each serving a specific purpose. The network architect must match the network fabric type with its primary function. Drag and drop the network fabric types from the left onto the corresponding descriptions on the right.

Answer:

Explanation:

NEW QUESTION # 70
Which components are part of the HyperFabric AI solution?

A. Cisco UCS servers combined with high-speed networking switches and third-party storage
B. integrated compute and networking hardware optimized for AI workloads, with optional cloud management
C. combination of Cisco networking hardware and software-defined storage designed for AI scalability
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 # 71
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?

A. Implement port-based VLANs for each project with router ACLs controlling storage access at the distribution layer.
B. Configure QoS-based traffic steering with separate DSCP markings per project and policy-based routing to shared storage.
C. Use VXLAN with separate VNIs per project and Group Policy Option to control granular access to shared storage.
D. Deploy separate physical networks for each project with dedicated switches and cross-connects to shared storage.

Answer: C

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 # 72
......

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