Which description best captures retrieval-augmented generation in an AI application?

Retrieving relevant enterprise content and supplying it to a generative model at request time

Why can network design become a bottleneck in distributed training?

Workers may need to exchange gradients or model state frequently across nodes

A company deploys eight new GPU servers for training, but utilization stays below 35 percent while storage read traffic saturates the uplinks. What should be investigated first?

End-to-end bandwidth from storage through the fabric to the training workers

How does redundancy differ from backup in an AI infrastructure environment?

Redundancy provides alternate live paths or components, while backup preserves data or configuration for recovery

What does latency describe in an AI network path?

The time required for data or a request to travel through a path and receive a response

A team sees distributed training stalls, rising pause-frame counters, and unrelated storage traffic slowing during synchronized GPU communication. What should be investigated first?

PFC scope, priority mapping, buffer thresholds, congestion spreading, and whether ECN or ETS tuning is needed

A GPU cluster carries RDMA data, storage replication, management, and backup traffic on the same fabric. Which QoS approach is most appropriate?

Map the traffic types into defined classes with suitable markings, queues, and bandwidth treatment

What is Remote Direct Memory Access intended to provide?

Direct memory-to-memory data movement between systems with reduced CPU involvement

Production training jobs take 40 percent longer after a storage network change. GPU telemetry shows idle periods during data loading, and fabric counters show no packet loss. What benchmark plan is most useful?

Run controlled storage and end-to-end data-loading benchmarks against the previous baseline while keeping workload inputs stable

A team wants alerts for fabric conditions that affect AI workload latency. Which practice is most appropriate?

Tune alerting around meaningful symptoms such as drops, congestion, link faults, and path anomalies

Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) Practice Test: 10 Free Questions (2026)

Exam-specific sample

Questions, answers, and option-by-option explanations

Each explanation identifies why the correct option fits and why the distractors do not.

Easy AI fundamentals and applications

Question 1

Which description best captures retrieval-augmented generation in an AI application?
1. Training a model from scratch on a new corpus before each user query
2. Compressing GPU tensors so a model can fit into less accelerator memory
3. Replacing a language model with a rules engine for deterministic responses
4. Retrieving relevant enterprise content and supplying it to a generative model at request time
Correct answer: D. Retrieving relevant enterprise content and supplying it to a generative model at request time
- A. Incorrect. RAG does not retrain the model from scratch for each question.
- B. Incorrect. Tensor compression is an optimization topic, not the defining purpose of RAG.
- C. Incorrect. RAG still uses a generative model; it does not replace the model with rules.
- D. Correct. RAG combines retrieval with generation so the model can answer using selected external context.
Medium AI fundamentals and applications

Question 2

Why can network design become a bottleneck in distributed training?
1. Workers may need to exchange gradients or model state frequently across nodes
2. Training workers avoid east-west traffic once the first epoch starts
3. Only north-south internet bandwidth affects model convergence
4. Distributed training relies on management-plane SSH latency for tensor exchange
Correct answer: A. Workers may need to exchange gradients or model state frequently across nodes
- A. Correct. Synchronization traffic can be frequent and bandwidth intensive in multi-node training.
- B. Incorrect. Distributed training can generate substantial east-west traffic during the job.
- C. Incorrect. Internet bandwidth is usually not the main factor for internal training synchronization.
- D. Incorrect. Tensor exchange uses the training communication stack, not SSH control sessions.
Hard Infrastructure components and architecture

Question 3

A company deploys eight new GPU servers for training, but utilization stays below 35 percent while storage read traffic saturates the uplinks. What should be investigated first?
1. The number of labels in the validation set before checking the infrastructure path
2. End-to-end bandwidth from storage through the fabric to the training workers
3. The spelling of model experiment names in the registry
4. The owner field assigned to the training project in the monitoring console
Correct answer: B. End-to-end bandwidth from storage through the fabric to the training workers
- A. Incorrect. Validation labels may matter for model quality, but they do not explain saturated uplinks and idle GPUs.
- B. Correct. The symptoms show accelerators waiting on data movement through a saturated path.
- C. Incorrect. Experiment names do not explain the bottleneck.
- D. Incorrect. Project ownership metadata has no bearing on storage-to-GPU throughput.
Medium Infrastructure components and architecture

Question 4

How does redundancy differ from backup in an AI infrastructure environment?
1. Redundancy stores archived data, while backup balances traffic across GPUs
2. Redundancy provides alternate live paths or components, while backup preserves data or configuration for recovery
3. Redundancy is used for model quality, while backup is used for packet forwarding
4. Redundancy applies to training, while backup applies only to inference
Correct answer: B. Redundancy provides alternate live paths or components, while backup preserves data or configuration for recovery
- A. Incorrect. The roles are reversed and inaccurate.
- B. Correct. Redundancy supports continued operation during failures; backups support restoration after loss or corruption.
- C. Incorrect. Redundancy and backup are infrastructure reliability concepts, not model-quality labels.
- D. Incorrect. Both concepts can matter across training and inference environments.
Easy Infrastructure components and architecture

Question 5

What does latency describe in an AI network path?
1. The total storage capacity available across the cluster for retaining model checkpoints and training artifacts
2. The time required for data or a request to travel through a path and receive a response
3. The number of GPUs installed in a server chassis
4. The amount of data a link can carry per second
Correct answer: B. The time required for data or a request to travel through a path and receive a response
- A. Incorrect. Storage capacity is not latency.
- B. Correct. Latency measures delay through a path or transaction.
- C. Incorrect. GPU count affects compute capacity but does not define network delay.
- D. Incorrect. Data carried per second is bandwidth.
Hard Deployment and data management

Question 6

A team sees distributed training stalls, rising pause-frame counters, and unrelated storage traffic slowing during synchronized GPU communication. What should be investigated first?
1. Whether the model has enough examples in the validation data set
2. Whether checkpoint files use a consistent naming pattern
3. PFC scope, priority mapping, buffer thresholds, congestion spreading, and whether ECN or ETS tuning is needed
4. Whether the training dashboard has too many panels open
Correct answer: C. PFC scope, priority mapping, buffer thresholds, congestion spreading, and whether ECN or ETS tuning is needed
- A. Incorrect. Validation data does not explain pause-frame counters.
- B. Incorrect. Checkpoint naming does not create pause storms.
- C. Correct. The symptoms point to congestion-control behavior affecting multiple classes.
- D. Incorrect. Dashboard use does not explain fabric-wide congestion.
Medium Deployment and data management

Question 7

A GPU cluster carries RDMA data, storage replication, management, and backup traffic on the same fabric. Which QoS approach is most appropriate?
1. Place the complete fabric into a single high-priority class to simplify policy
2. Map the traffic types into defined classes with suitable markings, queues, and bandwidth treatment
3. Mark backup traffic with the same value as RDMA traffic to avoid configuration drift
4. Remove packet markings at the server edge and rely on TCP retransmission behavior
Correct answer: B. Map the traffic types into defined classes with suitable markings, queues, and bandwidth treatment
- A. Incorrect. A single priority class hides congestion and can harm lower-priority operations.
- B. Correct. Distinct classes let the fabric protect sensitive traffic while still serving other flows.
- C. Incorrect. Backup traffic can consume bandwidth needed by latency-sensitive AI flows.
- D. Incorrect. TCP retransmission does not replace QoS classification and queuing.
Easy Deployment and data management

Question 8

What is Remote Direct Memory Access intended to provide?
1. A method for retaining model checkpoints for audit purposes
2. A user authentication workflow for cloud AI services
3. A scheduler policy that selects the newest GPU server
4. Direct memory-to-memory data movement between systems with reduced CPU involvement
Correct answer: D. Direct memory-to-memory data movement between systems with reduced CPU involvement
- A. Incorrect. Checkpoint retention is a storage policy.
- B. Incorrect. Authentication is an identity function.
- C. Incorrect. Scheduling policies do not define RDMA.
- D. Correct. RDMA allows data movement with less CPU overhead and lower latency when supported by the stack.
Hard Infrastructure operations and troubleshooting

Question 9

Production training jobs take 40 percent longer after a storage network change. GPU telemetry shows idle periods during data loading, and fabric counters show no packet loss. What benchmark plan is most useful?
1. Run controlled storage and end-to-end data-loading benchmarks against the previous baseline while keeping workload inputs stable
2. Change the model architecture and compare accuracy before checking infrastructure metrics
3. Run a power-only benchmark because power limits are the most likely cause of GPU idle time
4. Test a different dataset each run so the benchmark covers more examples
Correct answer: A. Run controlled storage and end-to-end data-loading benchmarks against the previous baseline while keeping workload inputs stable
- A. Correct. The evidence points to the data path, and controlled comparison against baseline is appropriate.
- B. Incorrect. Model architecture changes would obscure the infrastructure question.
- C. Incorrect. GPU idle time can come from data loading, storage, or scheduling, not just power.
- D. Incorrect. Changing datasets undermines repeatability.
Medium Infrastructure operations and troubleshooting

Question 10

A team wants alerts for fabric conditions that affect AI workload latency. Which practice is most appropriate?
1. Alert on every counter change regardless of impact so no event is missed
2. Disable historical data because current alerts are enough for troubleshooting
3. Tune alerting around meaningful symptoms such as drops, congestion, link faults, and path anomalies
4. Send only application alerts because network issues are visible to users first
Correct answer: C. Tune alerting around meaningful symptoms such as drops, congestion, link faults, and path anomalies
- A. Incorrect. Excessive noise causes alert fatigue and hides important signals.
- B. Incorrect. Historical data is valuable for incident correlation.
- C. Correct. Alerts should map to conditions that matter for workload health and operations.
- D. Incorrect. Application alerts alone may miss early network symptoms.

Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) Practice Test: 10 Free Questions

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