Cisco 300-640 DCAI Sample Questions & Practice Test

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.

300-640 DCAI snapshot

• Vendor: Cisco
• Exam code: 300-640
• Exam short name: DCAI
• Exam title: Implementing Cisco Data Center AI Infrastructure
• Associated certification path: CCNP Data Center concentration / Cisco data center AI infrastructure specialist path
• Exam time shown by Cisco: 90 minutes
• Current IT Mastery status: Sample questions

What these questions test

• distinguishing training, inference, RAG, generative AI, and other AI workload patterns
• matching AI workload requirements to network, compute, GPU, storage, and orchestration choices
• reasoning about high-performance data center fabrics, congestion controls, and latency-sensitive cluster behavior
• selecting monitoring and troubleshooting evidence for Cisco AI data center infrastructure
• recognizing operational constraints such as power, cooling, resilience, scalability, security, and hybrid placement

DCAI practice focus map

Sample Exam Questions

Try these 12 original sample questions for Cisco 300-640 DCAI. They are designed for self-assessment and are not official Cisco exam questions.

Question 1

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?

• A. A lossless, low-latency, high-bandwidth fabric that can sustain east-west GPU communication.
• B. A single low-cost management switch with no congestion visibility.
• C. Manual file transfer between training nodes after each epoch.
• D. A network design optimized only for north-south web traffic.

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.

Question 2

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?

• A. Batch backup workload.
• B. AI inference workload with latency-sensitive request/response behavior.
• C. Offline data archival workload.
• D. Firmware upgrade workflow.

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.

Question 3

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?

• A. GPU interconnect capability, server architecture, PCIe/NVLink-style paths, and data movement bottlenecks.
• B. Whether every user has local administrator access.
• C. The color of the rack labels.
• D. Whether the management VLAN name is short.

Best answer: A

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.

Question 4

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?

• A. Congestion management, queue behavior, ECN/PFC-related configuration where applicable, oversubscription, and traffic patterns.
• B. Browser bookmarks on the management workstation.
• C. Whether the application logo is cached.
• D. The desktop resolution of the operator console.

Best answer: A

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.

Question 5

Topic: storage design

A training workload repeatedly reads a large dataset and writes checkpoints. Which storage design concern is most relevant?

• A. Capacity only; throughput and access pattern do not matter.
• B. Storage throughput, latency, redundancy, scalability, checkpoint behavior, and data locality.
• C. Whether the storage array has a short hostname.
• D. Disabling backups to improve speed.

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.

Question 6

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?

• A. Workload orchestration that understands container placement, resource requests, health, and restart behavior.
• B. Manual SSH into each node for every job.
• C. A spreadsheet for all runtime decisions.
• D. No scheduling or health monitoring.

Best answer: A

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.

Question 7

Topic: monitoring

An AI job is slower than expected. Which evidence set is most useful before changing the architecture?

• A. GPU utilization, network throughput, congestion counters, storage latency, job logs, and orchestration events.
• B. The number of unused rack screws.
• C. A screenshot of the login page.
• D. The date the team purchased the servers.

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.

Question 8

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?

• A. Secure connectivity, data synchronization, identity, latency, cost, governance, and workload mobility.
• B. Only the cloud provider’s logo.
• C. Disable encryption because hybrid designs are complex.
• D. Move all data without reviewing policy.

Best answer: A

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.

Question 9

Topic: power and cooling

An AI cluster uses dense GPU servers and repeatedly hits thermal limits. Which non-network factor should be reviewed?

• A. Power delivery, rack density, cooling capacity, airflow, sustainability goals, and facility constraints.
• B. A new VLAN number.
• C. DNS suffix search order.
• D. Whether users have bookmarks to the dashboard.

Best answer: A

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.

Question 10

Topic: security

An AI infrastructure platform exposes model artifacts, datasets, and orchestration APIs. Which control set is most important?

• A. Identity controls, least privilege, network segmentation, secrets management, audit logging, and data protection.
• B. Shared admin credentials for all engineers.
• C. Public access to every API for easier testing.
• D. Disabling audit logs to reduce storage.

Best answer: A

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.

Question 11

Topic: RAG infrastructure

A retrieval-augmented generation application must ground responses in approved internal documents. Which infrastructure concern is most relevant?

• A. Data ingestion, indexing, access control, retrieval latency, source freshness, and auditability.
• B. Training every model from scratch for every question.
• C. Removing all source attribution.
• D. Letting the model retrieve any public internet result without policy.

Best answer: A

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.

Question 12

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?

• A. Correlate orchestration events with GPU, network, storage, and system telemetry during the failure window.
• B. Replace all switches without collecting evidence.
• C. Ignore the orchestration logs because only network counters matter.
• D. Disable monitoring to reduce noise.

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.

Related Cisco and AI routes

• Cisco ENCOR 350-401 for enterprise networking core practice.
• Cisco DevNet Associate / CCNA Automation 200-901 for API, automation, and programmability foundations.
• AWS AIF-C01 AI Practitioner for AI service and responsible AI fundamentals.
• AWS MLA-C01 Machine Learning Engineer Associate for applied machine learning workflow practice.

Official source

For current exam topics, duration, registration details, and Cisco policy changes, verify Cisco’s official 300-640 DCAI exam page before scheduling.