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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Infrastructure Operations20%- Monitoring and Management Tools
- Troubleshooting Common Issues
- Performance Optimization
- Capacity Planning
Topic 2: AI Infrastructure Security and Compliance10%- Access Control and Authentication
- Compliance Considerations
- Security Best Practices
Topic 3: AI Infrastructure Deployment25%- Hardware Installation and Configuration
- Cabling and Connectivity
- Storage Configuration for AI
- GPU Server Configuration
- Network Switch Configuration
Topic 4: AI Infrastructure Fundamentals15%- AI Workload Characteristics
- GPU Architecture and Types
- AI Infrastructure Requirements
- AI Ecosystem Components
Topic 5: 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 (Q68-Q73):

NEW QUESTION # 68
What is the process involved in workload scheduling for AI environments?

Answer: A

Explanation:
Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.


NEW QUESTION # 69
A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?

Answer: C

Explanation:
Cisco HyperFabric AI Small AI Cluster matches the 96-GPU requirement because the validated HyperFabric reference architecture uses 12 Cisco UCS C885A M8 servers with NVIDIA HGX for a total of 96 GPUs. Cisco Nexus HyperFabric is also cloud-managed and compliant with NVIDIA Enterprise Reference Architecture, enabling centralized management of the on-premises AI network, GPU servers, and storage.


NEW QUESTION # 70
What does workload distribution offer in an AI infrastructure with local and external resources?

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 # 71
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?

Answer: A

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 # 72
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: C

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 # 73
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