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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 - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge
AI Infrastructure Components and Architecture	30%	- Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration
AI Infrastructure Operations and Troubleshooting	20%	- Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
AI Infrastructure Deployment and Data Management	30%	- Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - 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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q80-Q85):

NEW QUESTION # 80
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.)

A. Insights
B. Monitor
C. Active CPU Utilization
D. GPU Memory Utilization
E. Explorer

Answer: C,D

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 # 81
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

A. Weighted Random Early Detection
B. Virtual output queuing
C. PortFast
D. NAT overload

Answer: B

Explanation:
Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.

NEW QUESTION # 82
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?

A. Cisco UCS X-Series Direct
B. Cisco UCS C845A M8 Server
C. Cisco Unified Edge
D. Cisco UCS C240 Servers

Answer: C

Explanation:
Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.

NEW QUESTION # 83
An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?

A. Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Disabled
B. Congestion Algorithm: Tail Drop
Congestion Notification: Enabled
C. Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Enabled
D. Congestion Algorithm: Tail Drop
Congestion Notification: Disabled

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 # 84
A corporate network administrator manages a HyperFabric AI environment and wants to receive email notifications about assertions in the fabric. The administrator sets up a recipient email endpoint under the notification endpoints in the controller, but notifications are not received.
Which two configurations cause the issue? (Choose two.)

A. The recipient email address is not verified by the HyperFabric controller.
B. The HyperFabric controller cannot reach the email server of the Acme Corporation.
C. The DNS server IP is not defined in the controller.
D. The email server configuration is not configured using the DNS name.
E. The fabric of interest and email endpoint are not configured in the default notification policy.

Answer: A,E

Explanation:
Email endpoints must be verified before they can receive HyperFabric notifications, and the endpoint must also be included with the relevant fabric in the notification policy. Creating the email endpoint alone does not activate assertion notifications for a fabric until both of these notification requirements are completed.

NEW QUESTION # 85
......

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