Free PDF Quiz 2026 300-640: Implementing Cisco Data Center AI Infrastructure–The Best Braindumps Downloads

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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q66-Q71):

NEW QUESTION # 66
A data center facility experienced a power outage, and the UCS X-Series servers did not restart automatically when power is restored. The configuration must be updated to prioritize these servers to boot as soon as possible to resume their work. Which set of power policy configurations must be used to meet these requirements?

Answer: B

Explanation:
The servers must be configured to power back on automatically after power is restored, so the restore behavior should be set to always power on. Setting the power priority to high ensures these UCS X-Series servers receive priority during power recovery, allowing them to resume workloads as quickly as possible.


NEW QUESTION # 67
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?

Answer: C

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 # 68
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?

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 # 69
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

Answer: B

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 # 70
What describes inference traffic patterns in AI deployments?

Answer: D

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 # 71
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