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Cisco 300-640 DCAI is the Implementing Cisco Data Center AI Infrastructure exam. It is associated with the CCNP Data Center path and focuses on the infrastructure behind AI workloads: network fabric, compute, GPUs, storage, orchestration, monitoring, and troubleshooting.
This page includes 12 original sample questions for initial review. Full Cisco DCAI practice is not live in IT Mastery yet; use the preview to confirm whether this is your target exam and use Notify me if you want updates for this route.
| Area | What to practise |
|---|---|
| AI fundamentals and applications | AI/ML workload types, lifecycle, use cases, and infrastructure components. |
| Infrastructure architecture | Network, compute, GPU, storage, orchestration, power, cooling, security, and hybrid placement decisions. |
| Deployment and data management | AI-ready fabrics, Cisco data center networking, UCS compute, storage, and orchestration patterns. |
| Operations and troubleshooting | Benchmarking, monitoring, system messages, Nexus Dashboard, Intersight, telemetry, and fault isolation. |
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Try these 12 original sample questions for Cisco 300-640 DCAI. Use them for study, self-assessment, and exam-scope review.
Topic: AI workload type
A team is designing infrastructure for a large model training job that exchanges gradients across many GPU nodes. Which infrastructure concern is most important?
Best answer: A
Explanation: Distributed training is sensitive to east-west bandwidth, latency, congestion, and packet loss. DCAI-style questions often test whether you understand that AI training clusters have different network demands from ordinary client/server traffic.
What this tests: Matching AI training behavior to data center fabric requirements.
Topic: training versus inference
An enterprise wants to host an interactive chatbot for employees. The model is already trained, but user response time must be low. Which workload description is most accurate?
Best answer: B
Explanation: Inference serves predictions or generated responses from an already trained model. It can be latency-sensitive, especially when users are waiting for interactive answers. Training and inference have different scale, communication, and performance profiles.
What this tests: Distinguishing AI workload types.
Topic: GPU connectivity
A node contains multiple GPUs that must exchange data at very high speed during model training. Which design factor should be reviewed?
Best answer: C
Explanation: AI infrastructure performance depends on both the network between nodes and the data paths inside each node. GPU interconnects, server architecture, memory bandwidth, and PCIe paths can all affect training performance.
What this tests: Recognizing compute and GPU infrastructure constraints.
Topic: congestion control
A data center AI fabric shows throughput drops during distributed training. Counters show congestion on links between leaf and spine switches. What should the engineer investigate?
Best answer: D
Explanation: AI workloads can stress data center fabrics with high-volume east-west traffic. Troubleshooting should focus on congestion signals, queueing, lossless Ethernet behavior where used, oversubscription, and workload placement.
What this tests: Troubleshooting high-performance AI network fabrics.
Topic: storage design
A training workload repeatedly reads a large dataset and writes checkpoints. Which storage design concern is most relevant?
Best answer: B
Explanation: AI workloads can be storage-intensive. Dataset reads, checkpoint writes, recovery requirements, and data locality all affect performance and resilience. Capacity alone is not enough.
What this tests: Matching storage architecture to AI workload behavior.
Topic: orchestration
A platform team needs to schedule AI workloads across GPU nodes, manage containers, and restart failed jobs. Which capability should be part of the design?
Best answer: C
Explanation: AI platforms commonly rely on orchestration to place workloads, manage resources, monitor health, and recover from failures. Manual node-by-node execution does not scale well.
What this tests: Understanding AI workload orchestration needs.
Topic: monitoring
An AI job is slower than expected. Which evidence set is most useful before changing the architecture?
Best answer: A
Explanation: AI infrastructure troubleshooting needs correlated evidence across compute, network, storage, orchestration, and application behavior. Guessing from one layer can lead to the wrong fix.
What this tests: Selecting troubleshooting evidence.
Topic: hybrid AI placement
A company wants to train models on-premises but use cloud services for burst capacity and selected data services. What should the architect evaluate?
Best answer: D
Explanation: Hybrid AI designs require technical and governance planning. Connectivity, data movement, identity, security, latency, and cost all affect whether the design is workable.
What this tests: Evaluating hybrid AI deployment constraints.
Topic: power and cooling
An AI cluster uses dense GPU servers and repeatedly hits thermal limits. Which non-network factor should be reviewed?
Best answer: B
Explanation: AI infrastructure is not only a networking problem. Dense GPU systems can require significant power and cooling planning, and facility limits can become the primary constraint.
What this tests: Recognizing physical data center constraints for AI systems.
Topic: security
An AI infrastructure platform exposes model artifacts, datasets, and orchestration APIs. Which control set is most important?
Best answer: C
Explanation: AI infrastructure includes valuable data, models, and control-plane APIs. Security must cover identity, network access, secrets, auditability, data protection, and operational boundaries.
What this tests: Applying security controls to AI infrastructure.
Topic: RAG infrastructure
A retrieval-augmented generation application must ground responses in approved internal documents. Which infrastructure concern is most relevant?
Best answer: D
Explanation: RAG depends on trustworthy retrieval. Infrastructure must support ingesting, indexing, securing, and refreshing approved sources while keeping response latency and auditability under control.
What this tests: Connecting AI application patterns to supporting infrastructure.
Topic: troubleshooting workflow
Users report intermittent AI job failures. The network team sees no obvious link down events, but orchestration logs show jobs failing during high cluster utilization. What is the best next step?
Best answer: A
Explanation: Intermittent AI infrastructure failures often require multi-layer correlation. DCAI-style troubleshooting should connect orchestration state, workload placement, compute pressure, storage behavior, and network telemetry before selecting a fix.
What this tests: Using cross-layer evidence for AI infrastructure troubleshooting.
For current exam topics, duration, registration details, and Cisco policy changes, verify Cisco’s official 300-640 DCAI exam page before scheduling.