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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q68-Q73):

NEW QUESTION # 68
What describes inference traffic patterns in AI deployments?

A. eliminates the need for data preparation
B. simplifies complex neural network architectures
C. optimizes data storage solutions for data lakes
D. provides real-time decision making

Answer: D

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 # 69
Drag and Drop Question

Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.

Answer:

Explanation:

NEW QUESTION # 70
An engineer made changes to the corporate DNS, which caused a Cisco UCS X-Series domain to lose connectivity to Intersight. To restore connectivity, the management interface DNS and NTP settings must be updated using the device console UI. Which configuration change must be made in Intersight to allow the updated DNS configuration to persist?

A. Create a device connector policy and apply it to the blade chassis.
B. Create a network connectivity policy and apply it to the blade chassis.
C. Edit the IMC access policy associated to the fabric interconnects.
D. Edit the domain profile deployed to the fabric interconnect.

Answer: D

Explanation:
In Cisco Intersight Managed Mode, the fabric interconnect management DNS and NTP settings are controlled through the UCS domain profile. If the settings are changed locally from the device console UI, the deployed domain profile must also be updated in Intersight so the corrected configuration is not overwritten during the next policy deployment or compliance remediation.

NEW QUESTION # 71
What is a purpose of Cisco AI PODs?

A. to provide a turnkey, full-stack infrastructure for AI training, fine-tuning, and inferencing
B. to deliver modular AI hardware components that can be integrated with third-party software platforms
C. to enable AI lifecycle management through cloud-native software with hardware support limited to compute servers
D. to offer a flexible AI infrastructure focused on inferencing workloads with optional training capabilities

Answer: A

Explanation:
Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.

NEW QUESTION # 72
An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?

A. VAST Data Universal Storage
B. Pure Storage FlashBlade
C. Local NVMe SSDs within each GPU server
D. NetApp All-Flash FAS (AFF)

Answer: A

Explanation:
VAST Data Universal Storage provides a software-defined, disaggregated shared-everything storage architecture designed for AI-scale workloads. It supports Cisco UCS-based deployments managed through Intersight and delivers high-performance all-flash storage with RDMA and GPU Direct Storage capabilities for large-scale AI training, fine-tuning, and inference environments.

NEW QUESTION # 73
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