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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q40-Q45):

NEW QUESTION # 40
Drag and Drop Question
An engineer must configure vNIC templates for a UCS X-Series deployment. Drag and drop the policy types from the left onto the corresponding configuration settings on the right. Not all options are used.

Answer:

Explanation:


NEW QUESTION # 41
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?

Answer: A

Explanation:
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.


NEW QUESTION # 42
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?

Answer: C

Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.


NEW QUESTION # 43
An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?

Answer: C

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 # 44
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

Answer: C

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 # 45
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