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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Infrastructure Fundamentals	20%	- 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
Topic 2: AI Infrastructure Deployment and Data Management	30%	- Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight
Topic 3: 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
Topic 4: AI Infrastructure Components and Architecture	30%	- 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 - Evaluate hybrid and multi-cloud AI integration

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q58-Q63):

NEW QUESTION # 58
What is the process involved in workload scheduling for AI environments?

A. aggregation of device statistics for reporting
B. optimization of licensing structures for compliance
C. generation of backup images for restoration operations
D. assignment of tasks to compute elements for optimized performance

Answer: D

Explanation:
Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.

NEW QUESTION # 59
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. 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
B. 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
C. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
D. 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

Answer: D

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 # 60
What does workload distribution offer in an AI infrastructure with local and external resources?

A. isolation of analytic functions within remote processing clusters
B. restriction of automation to local storage and performance measures
C. distribution of computational tasks across multiple locations
D. enforcement of fixed security policies on distribution endpoints

Answer: B

Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.

NEW QUESTION # 61
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: Tail Drop
Congestion Notification: Disabled
B. Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Enabled
C. Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Disabled
D. Congestion Algorithm: Tail Drop
Congestion Notification: Enabled

Answer: B

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 # 62
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 email server configuration is not configured using the DNS name.
B. The HyperFabric controller cannot reach the email server of the Acme Corporation.
C. The recipient email address is not verified by the HyperFabric controller.
D. The DNS server IP is not defined in the controller.
E. The fabric of interest and email endpoint are not configured in the default notification policy.

Answer: C,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 # 63
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