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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
AI Infrastructure Fundamentals	20%	- Describe AI use cases in data center environments - Describe AI and machine learning concepts - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio
AI Infrastructure Operations and Troubleshooting	20%	- Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Implement performance benchmarking and validation - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
AI Infrastructure Deployment and Data Management	30%	- Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads
AI Infrastructure Components and Architecture	30%	- Evaluate hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q19-Q24):

NEW QUESTION # 19
To support growing AI workload requirements, a regional medical practice is adding new X9516 X-Fabric module, UCS PCI Mezz cards, and UCS X580p PCIe nodes to their existing X-Series chassis. Which two administrative actions must be taken before the X-Fabric module is added to the chassis? (Choose two.)

A. Disconnect cabling between IFMs and fabric interconnects.
B. Power down all blades that would connect to the X-Fabric modules.
C. Power down all IFMs in the chassis.
D. Install UCS PCI Mezzanine cards in X-Series blades.
E. Disconnect device connector.

Answer: B,D

Explanation:
Before adding the X9516 X-Fabric module, the affected X-Series blades must be powered down because the X-Fabric connectivity path requires hardware installation and cannot be safely introduced while the connected compute nodes are running. The UCS PCI Mezzanine cards must also be installed in the blades so they can physically connect through the X-Fabric module to the X580p PCIe nodes for GPU expansion.

NEW QUESTION # 20
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

A. Manual CLI inspection once per week
B. NetFlow with hourly exports
C. Daily syslog review
D. Streaming telemetry with high-frequency sampling

Answer: D

Explanation:
Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.

NEW QUESTION # 21
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?

A. ARP
B. IGMP
C. LLDP
D. CDP

Answer: B

Explanation:
Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.

NEW QUESTION # 22
Drag and Drop Question
A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.

Answer:

Explanation:

NEW QUESTION # 23
A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment. Which Cisco Intersight capability is most critical for effectively monitoring this AI- centric infrastructure?

A. basic server health reporting, which is limited to CPU and memory statistics, and requires specialized third-party tools for any GPU-specific performance monitoring or diagnostics
B. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
C. comprehensive health and performance monitoring, which provides real-time telemetry data for UCS servers, including granular metrics for NVIDIA GPUs, which enables direct insights into AI workload execution
D. integration with Cisco Nexus Dashboard for advanced network flow analytics and anomaly detection across the data center fabric, which ensures high-speed data transfer between GPU nodes

Answer: C

Explanation:
Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.

NEW QUESTION # 24
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