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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q26-Q31):

NEW QUESTION # 26
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: C

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 # 27
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 # 28
What describes inference traffic patterns in AI deployments?

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 # 29
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)

Answer: A,E

Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.


NEW QUESTION # 30
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?

Answer: D

Explanation:
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.


NEW QUESTION # 31
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