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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate hybrid and multi-cloud AI integration - 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 |
| 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 |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues |
| AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
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NEW QUESTION # 46
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?
Answer: B
Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.
NEW QUESTION # 47
What is a primary behavior that occurs during the model training stage of the AI lifecycle?
Answer: C
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 # 48
What is a purpose of Cisco AI PODs?
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 # 49
What describes inference traffic patterns in AI deployments?
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 # 50
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?
Answer: C
Explanation:
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.
NEW QUESTION # 51
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