Cisco 300-640 Dumps

In Cisco Intersight Managed Mode, Fabric Interconnect physical port roles and port-channel definitions are configured through the Port Policy . Cisco's current Intersight configuration documentation states that a Port Policy configures ports and port roles on a Fabric Interconnect and permits administrators to enable or disable individual ports or port channels. Supported roles include Ethernet uplinks and Ethernet uplink port channels.

The Port Policy is subsequently associated with the UCS Domain Profile. Cisco documents that a Domain Profile configures an FI pair through reusable policies and controls ports, port channels, VLANs, and VSANs. During Domain Profile creation, the administrator attaches the appropriate Port Policy to each Fabric Interconnect.

A LAN Connectivity Policy is used primarily at the server-profile level to define vNIC connectivity. A Network Connectivity Policy configures DNS-related server-management settings rather than FI uplink topology. An Ethernet Network Group Policy defines VLAN groups associated with Ethernet connectivity and can be referenced by uplink configurations, but it does not itself create the FI port channel.

Consequently, the policy that must be changed to build the multiple-100-GbE uplink port channel is the Port Policy .

Study Guide Reference: AI Infrastructure Deployment and Data Management — Intersight Managed Mode, UCS Domain Profiles, Fabric Interconnect port configuration, and uplink resiliency.

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Inference is the operational phase in which a trained AI model receives new input and produces predictions, classifications, generated content, or other decisions. Cisco explicitly identifies Inference as an AI/ML workload type in DCAI objective 1.1.c. Unlike model training, which is dominated by iterative computation over large datasets, inference commonly handles production requests and is highly sensitive to response latency and throughput.

Cisco's AI POD design guidance states that inferencing workloads process live production traffic, often in real time, and evaluates them using metrics such as Time-to-First-Token, end-to-end request latency, inter-token latency, and requests per second. These characteristics directly support real-time decision making , making option C correct.

Option A concerns storage architecture rather than inference behavior. Option B incorrectly describes model architecture simplification, which inference does not inherently perform. Option D is also incorrect because inference pipelines can still require preprocessing, normalization, tokenization, or transformation of incoming data.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.1 AI/ML workload types — 1.1.c Inference.

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When an AI environment contains both local and external resources, workload distribution enables computational jobs to be placed or divided across multiple locations according to performance, capacity, latency, data residency, security, and cost requirements. This is a defining capability of hybrid AI infrastructure rather than an isolation mechanism.

Cisco defines hybrid AI infrastructure as combining on-premises and cloud resources within a common operating model. Workloads can use cloud capacity for burst training or experimentation while sensitive data or steady inference operations remain on premises. Cisco further identifies workload mobility, secure connectivity, and data synchronization as explicit requirements for hybrid AI deployment in DCAI objective 2.5. Cisco material on AI workload orchestration likewise describes workload execution across on-premises, cloud, and hybrid environments.

Option A unnecessarily isolates functions rather than distributing them. Option C contradicts hybrid automation by restricting operations locally. Option D describes security-policy enforcement, which is important but does not define workload distribution.

Accordingly, B is the technically correct response. It also corrects the answer indicated in the supplied source document, which lists C despite Cisco's current hybrid-infrastructure guidance.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.4.b Hybrid; 2.5 Hybrid AI deployment and workload mobility.

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A rail-optimized architecture is intentionally designed so that communication among corresponding GPU ranks within a single scalable unit can remain on the rail or leaf switches. This provides single-hop forwarding and avoids unnecessary traversal through spine switches, reducing latency for collective communication.

Spines become necessary when the deployment expands beyond one scalable unit. Cisco explicitly documents that multiple scalable units can be interconnected through leaf-and-spine switching. Traffic among GPUs inside one scalable unit benefits from rail-optimized single-hop communication, whereas traffic between GPUs located in different scalable units traverses the spine layer.

Therefore, option C precisely describes when spine switches provide architectural value: scaling the AI fabric beyond the boundaries of one scalable unit .

Option A is inaccurate because the rail design already provides the required local GPU bandwidth within the scalable unit; adding spines is not primarily intended to increase local rail bandwidth. Option B mischaracterizes the principal design purpose. Although additional topology can contribute to resiliency, spines are introduced fundamentally for interconnecting scalable units and enabling scale-out. Option D is also incorrect because increased buffering is not the architectural reason for introducing a spine tier.

Study Guide Reference: AI Infrastructure Components and Architecture — rail-optimized topology, scalable units, nonblocking network design, latency, and horizontal scaling.

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The Cisco UCS C885A M8 contains six 3000-W 54-V AC power supplies and normally supports an N+2 power configuration . In the scenario, three PSUs are connected to each independent power grid. Losing one complete grid therefore leaves only three operational 54-V PSUs.

Cisco's installation and service documentation specifies the resulting behavior precisely: when only three 54-V AC PSUs remain available, the server continues operating but enters a reduced-performance condition in which power to each GPU is capped at 60 percent . Cisco further states that four or more 54-V power supplies allow operation at full performance.

Consequently, the system does not immediately shut down, eliminating option A. A graceful operating-system shutdown is also not automatically initiated, eliminating D. Option C is incorrect because three active supplies are insufficient for full-performance operation on this platform.

This behavior allows the server to remain available during a significant power-domain failure while keeping its consumption within the capacity of the remaining PSUs. The operational tradeoff is reduced accelerator performance until adequate power redundancy is restored.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — UCS C885A M8 power redundancy, degraded operation, GPU power management, and availability.

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Retrieval-Augmented Generation enhances an LLM by retrieving relevant information from external knowledge sources and incorporating that material into the context supplied to the model during generation. This permits a pretrained model to answer questions using organization-specific information without requiring its underlying model parameters to be retrained for every knowledge update.

Cisco describes RAG as a technique that connects LLM applications to additional data sources, including internal repositories containing sensitive customer or business information. Cisco's enterprise RAG guidance specifically emphasizes using proprietary organizational data to ground responses in enterprise information. Therefore, providing access to proprietary data is the correct answer.

Option A is incorrect because RAG does not eliminate ingestion, parsing, chunking, cleaning, embedding, or indexing requirements. Option B is too absolute: grounding can improve factual relevance but does not inherently remove model bias. Option D describes modification of model parameters, which is characteristic of training or fine-tuning rather than standard RAG retrieval.

Cisco explicitly includes RAG as a required DCAI workload type, so candidates must distinguish retrieval-based augmentation from model training and fine-tuning.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.1.a RAG.

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ECN (Explicit Congestion Notification)

PFC (Priority Flow Control)

ECN and PFC address different congestion conditions in a RoCEv2 fabric and are intended to complement rather than replace each other. ECN is the appropriate mechanism for fabric-level congestion generated by incast patterns. When several GPU or storage senders simultaneously transmit toward the same destination, switch queues begin to build. ECN-capable switches mark packets rather than immediately dropping them, allowing the receiving endpoint to generate congestion feedback so the transmitting source can reduce its sending rate.

PFC provides hop-by-hop loss prevention for the designated lossless Ethernet priority. When an endpoint or downstream interface cannot accept a burst quickly enough and a queue approaches its configured threshold, PFC transmits pause frames for that specific priority. This temporarily stops upstream transmission and prevents packet loss.

Cisco's RoCEv2 designs combine ECN and PFC precisely because ECN provides proactive end-to-end congestion management while PFC acts as the protective mechanism when queue buildup becomes severe or occurs faster than endpoint rate adaptation can respond.

WRED can provide the queue-marking mechanism associated with ECN, but the requested logical congestion-control mechanism is ECN. AFD is not the corresponding RoCEv2 lossless mechanism for either description.

Study Guide Reference: AI Infrastructure Deployment and Data Management — congestion control, RoCEv2, ECN, PFC, QoS, and lossless Ethernet.

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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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In an Intersight Managed Mode UCS domain, DNS and NTP configuration is policy-controlled through the UCS Domain Profile deployed to the Fabric Interconnects. Changes entered locally through the Device Console can restore temporary reachability, but the policy configuration maintained by Intersight remains authoritative. If the Domain Profile still contains the previous DNS or NTP configuration, a subsequent deployment can overwrite the locally modified values.

Cisco documents that the Domain Profile references both the Network Connectivity Policy , which contains DNS settings, and the NTP Policy , which defines the NTP servers. The appropriate persistent remediation is therefore to edit the configuration associated with the deployed Domain Profile and redeploy it.

The IMC Access Policy applies primarily to server-management addressing and access rather than Fabric Interconnect DNS/NTP persistence. A Device Connector policy does not supply these domain DNS settings. Option C incorrectly applies a Network Connectivity Policy to the blade chassis; for the Fabric Interconnect domain, the relevant network-connectivity configuration is referenced through the UCS Domain Profile.

Cisco also advises that DNS and NTP should be configured on the management interface rather than being treated as Device Connector settings.

Study Guide Reference: AI Infrastructure Deployment and Data Management — Intersight Managed Mode, UCS Domain Profiles, DNS/NTP policies, and management connectivity.

Cisco's validated guidance for connecting VAST Data storage through an ACI fabric explicitly configures the RDMA storage class with Weighted Random Early Detection and Congestion Notification enabled . This makes option A directly consistent with Cisco's documented configuration.

In Cisco's VAST Data ACI design example, storage traffic arrives marked with DSCP 26 and is assigned to the Level2 QoS class. PFC is enabled for the corresponding no-drop DSCP, bandwidth is reserved for storage traffic, the congestion algorithm is configured as WRED, and congestion notification is enabled.

WRED and ECN-style congestion notification work together to detect developing queue pressure before buffers overflow. Rather than waiting for tail-drop conditions, the network can mark congestion-capable traffic so RoCEv2 endpoints reduce their transmission rates. PFC provides the complementary no-drop behavior when congestion becomes sufficiently severe.

Option B disables the required congestion signaling. Options C and D use Tail Drop, which Cisco's documented VAST RDMA storage class does not use. Tail Drop is instead shown in the same Cisco design for control-plane and ordinary traffic classes where lossless RDMA behavior is not required.

This configuration is therefore not merely a generic QoS preference; it directly follows Cisco's published ACI-to-VAST storage design.

Study Guide Reference: AI Infrastructure Deployment and Data Management — QoS, ECN/WRED, PFC, RDMA storage, and lossless fabric configuration.

The server contains six 3000-W supplies and must preserve N+2 redundancy . A 4,500-W workload requires two actively supplying PSUs because a single 3000-W supply cannot support the current load. Maintaining N+2 redundancy requires two additional PSUs to remain available beyond those required for the workload. The minimum operational complement is therefore four supplies: two supporting the power demand plus two retained for redundancy.

With six PSUs physically installed, the remaining two PSUs can be placed into standby , making option C correct.

The calculation is:

Required active capacity = ceiling(4500 W ÷ 3000 W) = 2 PSUs

Required redundancy = 2 PSUs

Minimum available PSUs = 2 + 2 = 4

Installed PSUs = 6

Eligible for standby = 6 − 4 = 2 PSUs

Power Save Mode is intended to reduce conversion losses when installed PSU capacity exceeds current demand. Cisco uses the same operating principle in UCS power management: additional supplies that are not needed to satisfy both the requested power budget and configured redundancy can be placed into power-saving operation.

Placing three supplies into standby would leave only three available supplies, violating the N+2 requirement for a workload requiring two PSUs.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — power efficiency, PSU redundancy, Power Save Mode, and UCS AI server power management.

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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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Cisco Unified Edge directly matches the distributed branch deployment requirements. The Cisco UCS XE9305 chassis contains two Unified Edge Management Controllers, providing redundancy for the chassis management plane. Its XE130c M8 compute node supports up to 768 GB of memory , precisely matching the stated in-memory processing requirement, and supports optional GPU acceleration.

Operationally, Unified Edge is designed for highly distributed sites where qualified IT personnel may not be present. Cisco documents remote server launch with no onsite IT staff, zero-touch provisioning, fleet visibility, and automated lifecycle management through Cisco Intersight. Intersight provides SaaS-based infrastructure lifecycle operations across distributed Unified Edge systems.

Although X-Series Direct is also suitable for remote locations, the exact combination of dual management controllers, 768-GB memory capacity, remote server launch, and Unified Edge's edge-focused lifecycle capabilities uniquely identifies option C. C845A M8 and C240 rack servers provide substantial compute capabilities but do not match the integrated edge chassis and zero-touch operational model required by the scenario.

This is a classic DCAI compute-selection question in which architectural and operational requirements must be evaluated together rather than selecting solely by raw compute performance.

Study Guide Reference: 2.2 Evaluate compute deployment; 2.5 Evaluate hybrid AI deployment with distributed infrastructure.

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The PFC pause-frame transmission rate per interface is the most directly relevant congestion indicator for an AI training fabric carrying RoCEv2 traffic. Priority Flow Control protects lossless traffic classes by transmitting pause frames when a receiving interface or switch queue approaches a congestion threshold. An elevated or sustained PFC pause rate therefore indicates that the lossless RDMA traffic class is repeatedly encountering queue pressure.

This is particularly significant for distributed AI training. GPU collective operations require synchronized communication between large numbers of accelerators, and excessive congestion can create tail latency, reduce effective network throughput, and extend Job Completion Time. Cisco AI networking guidance consequently treats PFC, ECN, queue occupancy, drops, and congestion telemetry as key operational signals for AI backend fabrics.

Spanning-tree topology changes are largely irrelevant in a modern routed or VXLAN-EVPN AI backend. BGP EVPN route counts are useful for control-plane capacity assessment but do not directly reveal congestion. VXLAN encapsulation rates measure traffic volume rather than whether buffers are becoming saturated.

PFC should not be interpreted as inherently problematic—some pause activity may occur during microbursts. However, unusually high or persistent pause-frame rates are a strong signal that congestion behavior requires investigation.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — RoCEv2 monitoring, PFC, congestion, telemetry, and AI training performance.

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Image recognition is a computer-vision application in which an AI model processes digital imagery and identifies meaningful visual content. Depending on the model and use case, the output can include image classifications, detected objects, faces, locations, labels, bounding boxes, or other semantic information describing what appears in the image. Therefore, detection and labeling of visual elements from digital sources is the correct result.

Cisco identifies computer vision as an established AI/ML workload and has used image-recognition techniques in technologies where models learn to identify objects despite variations in position and orientation. The DCAI exam blueprint requires candidates to understand AI use cases, making the distinction between computer-vision workloads and unrelated infrastructure functions important.

Option A is a security and compliance operation involving data anonymization. Option C is an infrastructure scheduling function generally performed by orchestration or management systems. Option D belongs to network control-plane and forwarding behavior and has no direct relationship to image recognition.

The key examination principle is that image recognition transforms unstructured visual input into meaningful classifications or detections that can subsequently drive automated decisions or analytics.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.3 Describe AI use cases.

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In Cisco Nexus Dashboard, a correlated anomaly is a secondary condition that Nexus Dashboard has associated through cause-and-effect analysis with another anomaly identified as the probable root cause. Cisco's current documentation explicitly distinguishes root-cause anomalies from correlated anomalies: the root anomaly triggers additional symptoms, and those dependent symptoms are displayed as correlated events.

For example, a physical or routing event may cause a downstream BGP session to fail. Rather than presenting each symptom as an independent failure, Nexus Dashboard analyzes relationships involving interfaces, devices, protocols, and timestamps. The BGP peer-down condition can therefore appear as correlated when a higher-level underlying anomaly explains it.

Option A incorrectly associates correlation with user-account activity. Option B describes repetition across several devices, which is not what the correlation indicator means. Option D refers generally to impact analysis rather than the specific root-cause relationship communicated by the label.

This feature reduces troubleshooting time because engineers can focus first on the root anomaly. Cisco specifically notes that resolving the underlying root cause should also resolve the anomalies correlated with that condition.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — Nexus Dashboard assurance, anomaly correlation, root-cause identification, and troubleshooting.

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VAST Data Universal Storage is the architecture explicitly associated with these requirements. Cisco's AI Data Platform documentation describes VAST's Disaggregated Shared-Everything (DASE) architecture, which separates stateless compute from persistent flash capacity so that storage performance and capacity can scale independently. This architecture is designed to eliminate conventional storage-controller bottlenecks and support extremely large AI datasets.

Cisco's AI POD with VAST architecture integrates Cisco UCS infrastructure, Cisco Intersight lifecycle management, high-performance Ethernet/RDMA networking, and VAST's all-flash storage platform. Cisco documentation also identifies UCS C225 M8 systems within VAST-based storage designs and describes the solution as optimized for high-throughput AI workloads.

GPU Direct Storage is particularly important because it reduces unnecessary CPU involvement and intermediate data copies when transferring datasets between storage and GPU memory. Combined with RDMA, this architecture can sustain the high parallel bandwidth required by large training and fine-tuning environments.

NetApp AFF and Pure Storage FlashBlade are capable enterprise storage products, but the DASE terminology uniquely identifies VAST. Local NVMe cannot provide the required exabyte-scale shared-storage architecture and would fragment datasets across individual GPU servers.

Study Guide Reference: AI Infrastructure Components and Architecture — high-performance storage, AI PODs, RDMA, scale, and shared AI data platforms.

VXLAN EVPN provides scalable logical segmentation without requiring physically separate fabrics. Separate VXLAN Network Identifiers can isolate individual AI training projects, while Cisco's Group Policy Option adds a security group identifier to the VXLAN header and enables granular microsegmentation through security-group policies. This makes it possible to isolate tenant or project traffic while selectively allowing access to common services such as centralized storage.

Cisco specifically describes GPO as supporting microsegmentation within VXLAN EVPN fabrics and granular traffic control between application segments. This approach preserves a high-performance shared fabric, avoiding the operational complexity and resource inefficiency of constructing physically separate networks for every project. Cisco also positions VXLAN EVPN as an architecture for AI/ML environments requiring segmentation, secure multitenancy, and scalable high-throughput communication.

Option A provides strong isolation but wastes switching resources and complicates shared-storage connectivity. Option C relies on conventional VLAN boundaries and centralized router ACL enforcement, introducing scaling and potentially additional forwarding constraints. Option D confuses QoS classification with security segmentation; DSCP identifies service classes but is not a security-isolation mechanism.

Study Guide Reference: 2.1 Evaluate network deployment based on bandwidth, latency, scalability, and security.

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