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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
| AI Infrastructure Components and Architecture | 30% | - 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 - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
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NEW QUESTION # 30
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 # 31
An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?
Answer: B
Explanation:
BGP EVPN provides scalable control-plane MAC and IP route advertisement for VXLAN fabrics.
It eliminates excessive flooding associated with traditional Layer 2 networks and supports efficient multi-tenant AI infrastructures. STP only prevents loops, OSPF is a Layer 3 routing protocol, and HSRP provides gateway redundancy rather than overlay endpoint learning.
NEW QUESTION # 32
What is a primary behavior that occurs during the model training stage of the AI lifecycle?
Answer: D
Explanation:
During model training, the model learns from training data by iteratively adjusting its internal weights and biases to reduce prediction error and improve performance against the defined objective function.
NEW QUESTION # 33
A medical company has existing third-party AI servers and NVMe storage, but they want to purchase Cisco network switches with a cloud managed feature for simplicity. Which solution best meets the requirements?
Answer: A
Explanation:
Bring Your Own AI HyperFabric is the best fit when the customer already has third-party AI servers and NVMe storage but wants Cisco 6000 Series switching with simplified cloud-managed fabric operations. The Premier license aligns with the AI-focused HyperFabric use case, while Cisco describes Nexus HyperFabric as a cloud-managed AI infrastructure solution using Cisco
6000 Series switches for AI workloads.
NEW QUESTION # 34
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?
Answer: B
Explanation:
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.
NEW QUESTION # 35
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