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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q15-Q20):

NEW QUESTION # 15
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?

Answer: A

Explanation:
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.


NEW QUESTION # 16
When considering a rail-only network design, when would adding spine switches make sense?

Answer: D

Explanation:
In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.


NEW QUESTION # 17
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 # 18
Which components are part of the HyperFabric AI solution?

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 # 19
An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?

Answer: D

Explanation:
VAST storage integration in a Cisco ACI fabric requires QoS settings that support lossless, congestion-aware Ethernet behavior. Weighted Random Early Detection with congestion notification enabled allows the fabric to signal congestion early through ECN before buffer exhaustion occurs, helping maintain predictable performance for high-throughput storage traffic.


NEW QUESTION # 20
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