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| Section | Objectives |
|---|---|
| Topic 1: Data Center Networking for AI Workloads | - Cisco Data Center networking technologies
|
| Topic 2: Compute and Storage for AI Infrastructure | - Compute architecture
|
| Topic 3: Security and Governance in AI Data Centers | - Infrastructure security
|
| Topic 4: AI Infrastructure Deployment and Operations | - Operations and lifecycle management
|
| Topic 5: Data Center AI Infrastructure Fundamentals | - AI/ML workload requirements in data centers
|
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NEW QUESTION # 76
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
Answer: C
Explanation:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.
NEW QUESTION # 77
A network engineer logs into Nexus Dashboard and sees an anomaly showing that a BGP peer connection has gone down. Next to the anomaly, a yellow bubble displays "Correlated." What does Nexus Dashboard indicate by "Correlated" in this case?
Answer: D
Explanation:
In Nexus Dashboard, a correlated anomaly means the displayed issue is associated with a higher-level root anomaly. The BGP peer-down event is being treated as a resulting or related symptom rather than the primary root cause, helping operators focus troubleshooting on the upstream issue first.
NEW QUESTION # 78
An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?
Answer: D
Explanation:
RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.
NEW QUESTION # 79
An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements:
- Support 256 GPU servers with RDMA connectivity.
- Prevent any packet loss that causes RDMA connection failures.
- Maintain consistent low-latency communication with a target of less
than 10 microseconds.
- Use industry-standard protocols and configurations.
Which configuration ensures that RoCEv2 operates as a lossless transport?
Answer: C
Explanation:
RoCEv2 requires lossless Ethernet behavior to prevent packet drops that can disrupt RDMA communication. Enabling Priority Flow Control on the traffic class carrying RoCEv2 packets allows Ethernet pause behavior for that priority, helping maintain reliable, low-latency RDMA transport across the AI training fabric.
NEW QUESTION # 80
A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?
Answer: C
Explanation:
Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.
NEW QUESTION # 81
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