Cisco Updated 300-640 Exam Questions and Answers by ekam

Traffic Analytics and Flow Telemetry are mutually exclusive flow-collection modes within Cisco Nexus Dashboard. Cisco's current Nexus Dashboard documentation identifies both under Flow Collection settings and states that Flow Telemetry must be disabled at the fabric level before Traffic Analytics can be enabled.

Traffic Analytics automatically discovers services and visualizes conversations based primarily on Layer 4 information. It provides visibility into network characteristics such as congestion, latency, and drops, which directly matches the monitoring requirements in the scenario. Cisco's Nexus Dashboard Insights guidance likewise states that if Flow Telemetry is already active, it must first be disabled for the relevant sites before Traffic Analytics is selected.

Delta Analysis is an assurance/comparison capability and is not the mutually exclusive collection mode described here. Connectivity Analysis performs reachability and path-oriented diagnostics. SNMP traps remain a separate event-notification mechanism and are not displaced by Traffic Analytics.

The operational distinction matters because Traffic Analytics and Flow Telemetry use different approaches to flow collection and analysis. An administrator cannot simply enable both collection modes simultaneously and expect them to operate in parallel on the same fabric configuration.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — Nexus Dashboard monitoring, telemetry, congestion analysis, latency, and packet-drop visibility.

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The surveillance workload creates a substantial persistent-capacity requirement across every location. Even at the stated minimum of ten cameras, each store generates approximately 240 GB per day and roughly 3.36 TB over fourteen days before considering filesystem overhead, redundancy, additional cameras, or growth. A scalable IP-based shared-storage architecture is therefore the appropriate option.

Cisco Unified Edge is designed around integrated high-speed Ethernet networking and supports network-accessible storage through standard IP mechanisms. Cisco's validated Unified Edge guidance describes network storage using protocols such as NFS and iSCSI , while explicitly noting that Fibre Channel is not validated in that design. Unified Edge also supports software-defined and distributed storage architectures carried over its Ethernet infrastructure.

Fibre Channel would introduce specialized SAN adapters, switches, cabling, and operational complexity that are generally inappropriate for a cost-sensitive, highly distributed retail edge. InfiniBand is optimized for specialized high-performance computing rather than economical video retention. Local storage could hold surveillance data at each store, but the question explicitly requires a centralized storage solution and long-term capacity that can be managed across locations.

IP-based shared storage therefore provides the best combination of scalability, standardization, cost efficiency, and Unified Edge compatibility.

Study Guide Reference: AI Infrastructure Components and Architecture — edge AI storage, IP storage, scalability, capacity, and deployment requirements.

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GPU Utilization and GPU Performance are the two choices that directly measure the behavior of accelerator resources used by AI workloads. GPU utilization indicates how actively the accelerator's computational resources are being consumed. Persistent low utilization can expose CPU, storage, networking, synchronization, or workload-scheduling bottlenecks, while sustained high utilization generally indicates effective use of expensive GPU capacity.

Cisco Intersight is increasingly oriented toward AI-specific infrastructure visibility. Cisco states that Intersight provides actionable metrics for GPU performance , resource-utilization trends, and power efficiency. Its Metrics Explorer and monitoring capabilities provide deep telemetry for infrastructure resources, including AI accelerators.

Explorer and Monitor in options B and C represent interfaces or functional capabilities rather than the performance metrics requested by the question. System CPU Utilization is useful when determining whether a workload is host-CPU constrained, but it does not directly measure GPU performance or GPU utilization.

Accordingly, A and D are the two choices that correspond most directly to the stated objective of measuring GPU performance and utilization.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — monitoring GPU performance, utilization, resource bottlenecks, and Cisco Intersight telemetry.

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An IP-based shared storage array is the appropriate architecture for a RAG environment containing approximately 50 TB of enterprise documents. RAG requires compute nodes to access a common body of source documents, embeddings, vector indexes, model artifacts, and intermediate datasets. With six UCS X210c servers participating in the solution, centralized shared storage avoids creating isolated data silos on individual compute nodes and allows the full AI cluster to access the same authoritative dataset.

Cisco AI POD and RAG architectures use enterprise shared storage connected through high-speed Ethernet. Cisco validates IP-based protocols including NFS, iSCSI, NVMe/TCP, and—in higher-performance AI platforms—RDMA-capable storage designs. Cisco's RAG architecture with FlexPod, for example, uses NFS, iSCSI, and NVMe/TCP storage networks, while Cisco AI PODs support external storage from partners such as NetApp, Pure Storage, and VAST Data.

JBOD and RAID 5 local storage remain tied to individual servers and do not provide the desired shared data layer. Tape provides economical archival retention but lacks the random-access latency and throughput required for active RAG retrieval.

Study Guide Reference: AI Infrastructure Components and Architecture — storage selection, AI data access, scalability, RAG infrastructure, and shared storage.

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