300-640 Implementing Cisco Data Center AI Infrastructure Exam Topics and Questions

This topic sets the foundation for everything else in the exam. You face questions on workload types, the AI lifecycle, and the infrastructure models that support AI at different scales. The cost comes in distinguishing between similar concepts under pressure, particularly where workload types overlap or where deployment models share characteristics.

– Workload types and the AI lifecycle

RAG, training, inference, and generative AI each place different demands on infrastructure, and the exam tests whether you know which resources each one consumes. Training is compute-intensive and long-running. Inference is latency-sensitive and scales horizontally. RAG combines retrieval with generation, so it stresses both storage throughput and model serving. Generative AI spans multiple workload types depending on whether you are building the model or using it. The AI lifecycle connects data preparation, model development, training, validation, deployment, and monitoring. Each phase has its own bottleneck, and questions probe whether you can identify which phase is failing when performance degrades or results drift.

– Use cases and deployment models

AI use cases range from natural language processing to computer vision, recommendation engines, and anomaly detection. The exam expects you to map a use case to its infrastructure requirements, not to explain the algorithms behind it. Deployment models include cloud, hybrid, on-premises, and edge AI. Cloud offers elasticity but introduces latency and data sovereignty concerns. Hybrid splits workloads between on-premises training and cloud inference, or the reverse. On-premises keeps data local and gives you control over the hardware stack. Edge AI pushes inference to the network perimeter, trading model size and accuracy for response time. Questions turn on which model fits a given constraint, such as regulatory requirements, bandwidth limits, or real-time response needs.

– Components and Cisco solutions

AI environments depend on network, compute, storage, virtualization, containerization, orchestration, and monitoring. Network design affects collective communication patterns during training. Compute includes GPU deployment and NVLink for inter-GPU bandwidth. Storage spans SAN, Fibre Channel, NVMe, block, and file, each with different latency and throughput profiles. Virtualization and containerization isolate workloads and enable portability. Orchestration tools schedule jobs and manage resources. Monitoring tracks utilization, errors, and performance drift. Cisco AI solutions include AI PODs, which are validated reference architectures; AI Canvas, which provides a software layer for workload orchestration; and Hyperfabric AI, which delivers low-latency, high-bandwidth networking for GPU clusters. The exam tests whether you know what each solution does and when to apply it.

How AI Fundamentals and Applications is tested

With 20 percent of the exam, this topic generates one in five items. Questions present a scenario with workload characteristics, constraints, or performance problems, then ask you to choose the workload type, lifecycle phase, deployment model, or component that fits. The challenge is in the details: two options often look plausible until you check them against bandwidth, latency, data residency, or cost requirements. Candidates lose marks when they pick a deployment model that solves one constraint but violates another, or when they confuse training with inference and recommend infrastructure that cannot handle the actual workload. Another trap is assuming that every AI project needs the same architecture. A generative AI application serving millions of users has different needs than a research team training a model once a month. Read the scenario for the constraint that eliminates three of the four options, then verify that your choice satisfies all the stated requirements.

Where the first topic introduced the concepts, this one asks you to evaluate deployments against workload requirements. The subtopics cover network, compute, storage, power and sustainability, and hybrid cloud integration. Each evaluation turns on multiple factors, and the exam tests whether you can prioritize them correctly when they conflict. A network design might meet bandwidth requirements but fail on latency. A compute deployment might scale well but lack the GPU interconnect needed for distributed training. Storage might deliver high throughput but sacrifice redundancy. Power and cooling might support peak load but push your PUE beyond acceptable limits. Hybrid deployments add complexity around secure connectivity, data synchronization, and workload mobility. The cost here is in the tradeoffs: you rarely get to optimize for everything at once, and the exam penalizes candidates who ignore the requirement that matters most in the scenario. Because the vendor has not yet published a detailed breakdown of subtopics, expect questions that integrate multiple evaluation dimensions within a single item. You might be given a workload profile and asked which deployment choice satisfies bandwidth, redundancy, and cost constraints simultaneously. Or you might be presented with a hybrid architecture and asked to identify the weak point in data synchronization or connectivity. The 30 percent weighting makes this the heaviest topic on the exam, so gaps here cost you more than gaps elsewhere.

How AI infrastructure Components and Architecture is tested

At 30 percent, this topic generates nearly one in three items. Questions describe a workload with specific requirements, then ask you to evaluate whether a proposed deployment meets them or to choose the deployment that best fits. The challenge is that requirements often conflict. A design that maximizes performance might fail on redundancy. A design that achieves high availability might exceed the power budget. The exam expects you to identify which requirement is non-negotiable, eliminate options that violate it, then choose among the survivors based on the secondary constraints. Candidates lose marks when they optimize for the wrong dimension or when they assume that more of everything is always better. A GPU cluster with NVLink might be overkill for inference workloads that need horizontal scale, not tighter coupling. A storage array optimized for capacity might bottleneck a training job that depends on IOPS. Another common mistake is ignoring hybrid deployment complexity. Secure connectivity is not the same as fast connectivity, and workload mobility requires more than just network bandwidth. Read every requirement in the scenario, rank them by impact, and verify that your choice satisfies the top two or three before you move on.

This topic moves from evaluation to implementation. You configure high-performance networks using Cisco Data Center technologies, set up compute and storage using Cisco UCS, and deploy AI-ready fabrics using orchestration tools. The weight matches the previous topic at 30 percent, and the focus is on the specific settings, policies, and mechanisms that make AI workloads run correctly. Network configuration includes congestion control with PFC, ECN, and ETS; RDMA over Converged Ethernet in both RoCE and RoCEv2 variants; quality of service policies; and load distribution. Compute and storage configuration covers UCS domain profiles, power policies, storage policies, LAN connectivity and vNIC policies, QoS policies and system classes, and NTP policies. Fabric deployment uses Nexus Dashboard, APIC, Hyperfabric, and Intersight. The exam tests whether you know which setting to change when a workload underperforms, when traffic is dropped, or when synchronization fails. Candidates who have worked with these tools in a lab environment have an advantage, because the questions assume familiarity with the policy hierarchy and the effect of each parameter. If you have only read about the technologies, you will struggle with items that describe a misconfiguration and ask you to identify the fix. The subtopic list is detailed, and every mechanism and policy type listed can appear in an item. You cannot skip congestion control or QoS and expect to pass, because those are the mechanisms that prevent AI traffic from overwhelming the network during collective operations. Similarly, UCS policies control how compute resources are allocated, and getting them wrong means workloads either fail to start or run inefficiently.

How AI infrastructure Deployment and Data Management is tested

At 30 percent, this topic shares the highest weighting and generates as many items as the architecture topic. Questions present a deployment scenario, a configuration excerpt, or a symptom such as packet loss or failed storage access, then ask you to choose the correct configuration step or identify the misconfigured parameter. The challenge is in the specificity. You need to know not just that PFC exists, but when to enable it and what happens when it conflicts with ECN. You need to know the difference between RoCE and RoCEv2, and which one works across routed networks. You need to know which UCS policy controls vNIC placement and which one controls QoS markings, because the exam will describe a problem that could be caused by either and ask you to pick the right fix. Candidates lose marks when they choose a plausible-sounding option that addresses a different layer of the problem, or when they apply a fix that works in general-purpose data centers but breaks AI workloads. Another trap is assuming that default settings are sufficient. AI traffic patterns differ from enterprise application traffic, and the default QoS or congestion control behavior often needs tuning. Read the scenario for clues about what the workload is doing when the problem occurs, match that to the mechanism responsible, then verify that your chosen fix aligns with the observed symptom.

The final topic covers operations and troubleshooting after deployment. You implement benchmarks to evaluate infrastructure performance, monitor AI data center infrastructures using Cisco solutions such as Nexus Dashboard and Intersight, and troubleshoot problems using system messages and management tools. The subtopics address operational telemetry, system health, alerts, and log correlation. At 20 percent, this topic generates one in five items, the same weight as the fundamentals topic. The difference is that operations questions assume you have already deployed the infrastructure and now need to keep it running and diagnose failures when they occur. Benchmarks matter because AI workloads behave differently under load, and you need a baseline to know whether performance has degraded. Monitoring tools collect telemetry on network throughput, GPU utilization, storage IOPS, power draw, and temperature. Alerts notify you when thresholds are breached, but the exam tests whether you can distinguish between an alert that indicates a real problem and one that reflects normal variance during a training run. Log correlation is critical when a failure spans multiple components, such as a job that fails because storage became unavailable during a checkpoint operation, which happened because a network link flapped, which happened because of a misconfigured policy. Troubleshooting items describe a symptom and a set of observations, then ask you to identify the root cause or the next diagnostic step. Candidates who work methodically and check the most likely failure points first will do better than those who jump to conclusions based on one data point.

How AI infrastructure Operations and Troubleshooting is tested

With 20 percent of the exam, this topic generates one item in five. Questions describe a performance problem, a failure, or an alert condition, then ask you to choose the correct diagnostic step, identify the root cause, or select the tool or log source that will give you the information you need. The challenge is in the layering. A training job that runs slowly could be bottlenecked by GPU utilization, network congestion, storage throughput, or CPU scheduling, and the exam expects you to narrow the field using the telemetry and logs provided in the scenario. Candidates lose marks when they pick a cause that matches one symptom but ignores others, or when they recommend a diagnostic step that cannot distinguish between the remaining possibilities. Another trap is assuming that the first anomaly you see is the root cause. A spike in network retransmits might be a symptom of congestion, but the congestion might be caused by a misconfigured QoS policy or by a failed link that forced traffic onto a narrower path. The exam rewards candidates who trace the chain of events backward from the symptom to the underlying failure. Read the scenario carefully, note which telemetry sources are mentioned, and choose the answer that explains all the observations, not just the most obvious one.