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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration |
| AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
| AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
| AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability |
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NEW QUESTION # 39
An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?
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 # 40
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?
Answer: D
Explanation:
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.
NEW QUESTION # 41
A network engineer must secure management access to Cisco Nexus AI switches. Which protocol should replace Telnet to provide encrypted administrative sessions?
Answer: B
Explanation:
SSH provides encrypted remote management sessions and protects credentials and configuration traffic from interception. Telnet transmits data in plaintext and is unsuitable for secure AI data center environments. FTP and TFTP are file transfer protocols, while SNMPv1 lacks modern security protections.
NEW QUESTION # 42
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 # 43
What describes inference traffic patterns in AI deployments?
Answer: B
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 # 44
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