Free Practice Questions for the Cisco CCNP Data Center 300-640 Exam (2026 Updated)

RoCEv2 traffic must be mapped to a no-drop QoS class when lossless Ethernet operation is required. Cisco's current Intersight VIC configuration guidance provides an explicit configuration for this requirement: create a System QoS policy, select Platinum as the priority, and clear the Allow Packet Drops checkbox. The policy is then associated with the UCS Domain Profile.

Cisco's storage best-practice documentation gives the same configuration for NVMe over RoCEv2: Platinum enabled, Allow Packet Drops unchecked, and a jumbo MTU—commonly 9216 at the system-class level. This establishes the UCS-side no-drop class required for RDMA traffic.

Disabling Platinum, as proposed in option A, would remove the intended lossless traffic class. Enabling every system class in option B does not automatically make RoCEv2 lossless because packet-drop behavior remains a property of the selected class. Disabling Gold in option C is unrelated to ensuring that the RoCE traffic class receives no-drop treatment.

A complete RoCE design must maintain consistent QoS classification and no-drop handling end to end. However, for the System QoS policy requested here, option D is the precise Cisco configuration.

Study Guide Reference: AI Infrastructure Deployment and Data Management — RoCEv2, PFC, lossless Ethernet, and UCS System QoS.

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Cisco UCS X-Series is the strongest fit because it is designed as a modular, scalable compute platform that can support conventional enterprise workloads and GPU-accelerated AI workloads within the same architectural framework. Cisco explicitly positions UCS X-Series for workload consolidation, allowing compute nodes and GPU resources to be added as requirements evolve without replacing the entire infrastructure. The platform supports substantial memory capacity, virtualization, GPU acceleration, and centralized lifecycle management through Cisco Intersight.

The current UCS X-Series portfolio supports X210c and X215c compute nodes, X580p GPU expansion, and X-Fabric connectivity. Cisco further positions the platform for AI workloads through high-bandwidth, low-latency GPU connectivity and scalable GPU and CPU configurations.

UCS M-Series is not the current strategic modular platform for these requirements. Unified Edge is optimized for distributed edge deployments rather than a large, expanding enterprise data center. A standalone C-Series deployment can provide powerful GPU servers, but it does not offer the same chassis-level composability and resource expansion model described in the scenario.

Study Guide Reference: 2.0 AI Infrastructure Components and Architecture — 2.2 Evaluate compute deployment based on CPU, GPU, memory, virtualization, scalability, redundancy, and workload requirements.

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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.

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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A rail-optimized topology is specifically engineered for large GPU clusters where collective communication performance and minimum inter-GPU latency are critical. In a rail-optimized design, corresponding GPU/NIC interfaces across compute nodes attach to the same rail switch. This allows communication between GPUs of the same rank to remain on a single leaf or rail switch whenever possible, eliminating unnecessary spine traversal and reducing network hops.

Cisco's AI/ML networking guidance gives a directly applicable design example: a rail-optimized, nonblocking architecture connecting 512 GPUs in 64 nodes, with eight GPUs per node . The design uses eight rail/leaf switches and four spine switches and maintains nonblocking bandwidth while optimizing GPU collective communication. Cisco explicitly states that rail-optimized design is recommended for NVIDIA GPU environments.

A conventional leaf-spine architecture can certainly provide high performance and nonblocking bandwidth, but option C is more specific to the requirement for the lowest possible GPU-to-GPU latency at this scale. Flat Layer 2 does not provide the required scalable fabric architecture, while a traditional three-tier model introduces additional hops and potential oversubscription.

Study Guide Reference: 2.0 AI Infrastructure Components and Architecture — 2.1 Evaluate network deployment based on bandwidth, latency, redundancy, scalability, and security.

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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Two settings determine the required behavior. First, Power Restore must be configured as Always On . Cisco defines this setting as instructing the server to power on after power is restored following a loss event. Immediate is not one of the documented Power Restore states; supported states include Last State, Always On, and Always Off.

Second, Power Priority should be High . Cisco explains that Power Priority determines initial power allocation among servers. A server assigned a higher priority receives preference when power is being allocated, which directly supports the requirement that these systems restart as soon as possible. Cisco validated designs explicitly show Power Profiling enabled with Power Priority set to High for performance-oriented deployments.

Power Profiling permits the platform to determine server power requirements. The Processor Package Power Limit should remain at Default for the X-Series scenario. Cisco documentation also notes that the selectable PPL setting is specifically applicable to particular C225/C245 M8 systems rather than being the mechanism for prioritizing X-Series restart behavior.

Option C provides Always On but fails to assign High power priority. Options A and D contain the invalid Immediate restore setting.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — UCS power management, recovery after power loss, power prioritization, and infrastructure availability.

Cisco Nexus 9000 integration with NVIDIA adaptive-routing capabilities requires the Nexus switch to enable the Spectrum-X hardware profile . Cisco's current NX-OS configuration guide explicitly specifies the global configuration command hardware profile spectrum-x to enable NVIDIA Spectrum-X adaptive routing. When a Nexus switch peers with a supported NVIDIA NIC, this capability allows the NIC to handle packet reordering created by granular techniques such as per-packet Dynamic Load Balancing.

This is particularly important for AI training because large synchronized RDMA flows can create uneven utilization under conventional hash-based ECMP. Cisco explains that per-packet DLB can spray packets over multiple paths and that NVIDIA adaptive routing/direct data placement enables the receiver to correctly handle packets that arrive out of order, improving utilization and collective-operation performance.

Option A is not the documented NX-OS configuration syntax. Option B is not the Cisco-prescribed Nexus integration command. Option D may be relevant to traffic classification in other designs but does not activate Spectrum-X adaptive-routing interoperability.

Therefore, the Cisco-documented configuration is C. This differs from the supplied file's marked answer B.

Study Guide Reference: 3.0 AI Infrastructure Deployment and Data Management — 3.1.d Load distribution.

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Cisco Nexus Hyperfabric's full-stack AI infrastructure integrates compute, GPU acceleration, high-performance networking, optional enterprise storage, and centralized cloud-based lifecycle management. Cisco identifies the principal components as the Nexus Hyperfabric cloud controller, Cisco 6000/Nexus switching platforms, Cisco UCS GPU/DPU servers equipped with NVIDIA accelerators, and optional Cisco UCS-based VAST storage.

Option C therefore contains the essential architecture: Cisco UCS servers with NVIDIA GPUs, Cisco switching, optional VAST Data storage, and cloud-managed networking .

Option A omits the cloud-controller architecture and does not specifically identify the NVIDIA-based accelerated compute model that characterizes the full-stack AI solution. Option B incorrectly describes cloud management as optional. Cloud-based fabric design, deployment, visibility, and lifecycle control are fundamental elements of Nexus Hyperfabric. Option D omits GPU compute entirely and reduces the solution to networking and software-defined storage, which does not represent the integrated AI stack.

Cisco's architecture is intended to reduce the complexity associated with constructing AI infrastructure manually. Validated cluster templates coordinate compute, networking, storage, optics, cabling, and lifecycle operations while aligning with NVIDIA enterprise AI reference architectures.

Study Guide Reference: AI Infrastructure Components and Architecture — Cisco Hyperfabric, GPU compute, AI networking, storage integration, scalability, and cloud management.

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A high-performance Cisco UCS architecture normally uses redundant fabric interconnects to provide both high availability and aggregated bandwidth. Cisco documentation describes Fabric Interconnects as typically deployed as a pair, integrating UCS components into a highly available management and connectivity domain. Modern UCS X-Series connectivity can provide up to 200 Gbps aggregate unified-fabric bandwidth per compute node through dual 100-Gbps paths.

Option D therefore aligns with UCS architecture: dual fabrics eliminate the single point of failure associated with one Fabric Interconnect, while 100-Gbps connectivity supports high-throughput AI inference workloads. Port channels on uplinks provide bandwidth aggregation and link-level resiliency toward the upstream data center network.

Option A incorrectly limits the Fabric Interconnect to management traffic; UCS unified fabric carries production Ethernet, storage, and management-related connectivity. Option B bypasses the integrated UCS fabric architecture and unnecessarily separates management onto Catalyst infrastructure. Option C introduces both a single point of failure and a severe bandwidth constraint by using one FI and 10-Gbps links.

For AI inference, where throughput and deterministic latency directly influence application performance, redundant high-speed fabric design is the appropriate implementation.

Study Guide Reference: 3.0 AI Infrastructure Deployment and Data Management — 3.2 Configure high-performance compute and storage using Cisco UCS; 3.2.d LAN connectivity and vNIC policies.

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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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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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Priority Flow Control is a foundational mechanism used to provide lossless Ethernet behavior for RoCEv2. PFC operates on individual Ethernet priorities and allows a congested receiver or switch to pause transmission for the affected priority while permitting traffic in other classes to continue. Cisco's AI networking guidance explicitly states that PFC is enabled for RoCEv2 traffic to prevent packet drops caused by congestion.

This characteristic is critical for RDMA workloads because packet loss can trigger retransmission behavior and significantly degrade GPU collective communication performance. In an AI backend fabric, RoCEv2 is normally placed in a no-drop QoS class with PFC configured for that traffic class. ECN is commonly used alongside PFC to provide proactive congestion signaling, but PFC is the mechanism that directly prevents loss when buffers approach critical utilization.

Traffic shaping in option A may moderate burst rates but does not establish lossless transport. A high DSCP marking in option C provides classification and forwarding preference, not loss prevention. Weighted fair queuing in option D allocates scheduling bandwidth but also does not guarantee that packets will not be discarded during congestion.

Study Guide Reference: 3.1.a Congestion control mechanisms — PFC, ECN, ETS; 3.1.b RoCE/RoCEv2; 3.1.c QoS.

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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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Cisco AI PODs provide a prevalidated, integrated full-stack infrastructure platform for enterprise artificial intelligence. Cisco describes AI PODs as combining Cisco UCS compute, Cisco Nexus networking, accelerated GPUs, enterprise storage, management, orchestration, and supporting software into an architecture designed for the complete AI workload lifecycle. The supported workload spectrum explicitly includes model training, fine-tuning, and high-throughput inference.

Option D therefore captures both the architectural scope and workload intent. The principal value of AI POD is that organizations do not need to independently design and validate every server, accelerator, network, storage, and software dependency. Cisco supplies validated designs and modular scale-unit architectures that accelerate deployment while retaining enterprise operational characteristics.

Option A is too restrictive because AI POD is not merely cloud-native lifecycle software and its hardware scope extends far beyond compute. Option B incorrectly makes inferencing the primary focus; Cisco AI POD supports the complete AI lifecycle and can be purpose-built for training, inference, or mixed workloads. Option C understates the architecture by describing it merely as modular hardware components. Cisco explicitly characterizes AI POD as an integrated full-stack solution.

Study Guide Reference: AI Infrastructure Components and Architecture — Cisco AI infrastructure solutions, compute, networking, storage, GPU acceleration, and scalable architectures.

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