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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
AI Infrastructure Operations and Troubleshooting20%- Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
- Manage health, logs, and events
- Troubleshoot network, compute, storage, and orchestration issues
- Optimize performance, security, and reliability
- Implement performance benchmarking and validation
AI Infrastructure Fundamentals20%- Describe AI and machine learning concepts
- Describe AI use cases in data center environments
- Describe types of AI infrastructure: on-premises, cloud, hybrid, edge
- Explain Cisco AI infrastructure solutions and portfolio
- Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring
AI Infrastructure Deployment and Data Management30%- Implement data management and lifecycle for AI workloads
- Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics
- Configure compute and storage using Cisco UCS: profiles, policies, Intersight
- Integrate AI frameworks and applications with Cisco infrastructure
- Deploy and manage AI infrastructure with orchestration tools
AI Infrastructure Components and Architecture30%- Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS
- Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability
- Evaluate power, cooling, and efficiency requirements
- Evaluate hybrid and multi-cloud AI integration
- Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q68-Q73):

NEW QUESTION # 68
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

Answer: D

Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


NEW QUESTION # 69
Which components are part of the HyperFabric AI solution?

Answer: B

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 # 70
A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?

Answer: A

Explanation:
VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.


NEW QUESTION # 71
Which function is provided through natural language processing?

Answer: C

Explanation:
Natural language processing enables systems to interpret, process, and transform human language from spoken or written inputs into structured data that applications can analyze, search, classify, or use for automation.


NEW QUESTION # 72
A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?

Answer: C

Explanation:
Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.


NEW QUESTION # 73
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