Try 10 focused Cisco 300-640 DCAI questions on AI Fundamentals and Applications, with explanations, then continue with IT Mastery.
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| Field | Detail |
|---|---|
| Exam route | Cisco 300-640 DCAI |
| Topic area | AI Fundamentals and Applications |
| Blueprint weight | 20% |
| Page purpose | Focused sample questions before returning to mixed practice |
Use this page to isolate AI Fundamentals and Applications for Cisco 300-640 DCAI. Work through the 10 questions first, then review the explanations and return to mixed practice in IT Mastery.
| Pass | What to do | What to record |
|---|---|---|
| First attempt | Answer without checking the explanation first. | The fact, rule, calculation, or judgment point that controlled your answer. |
| Review | Read the explanation even when you were correct. | Why the best answer is stronger than the closest distractor. |
| Repair | Repeat only missed or uncertain items after a short break. | The pattern behind misses, not the answer letter. |
| Transfer | Return to mixed practice once the topic feels stable. | Whether the same skill holds up when the topic is no longer obvious. |
Blueprint context: 20% of the practice outline. A focused topic score can overstate readiness if you recognize the pattern too quickly, so use it as repair work before timed mixed sets.
These original IT Mastery practice questions are aligned to this topic area. Use them for self-assessment, scope review, and deciding what to drill next.
Topic: AI Fundamentals and Applications
An autonomous inspection line requires defect decisions within 25 ms. After a redeploy, cameras still stream, but reject actions occur late. Intersight shows the edge UCS node is healthy and GPU utilization is below 10%. The plant WAN link to the central data center spikes during camera bursts, and the orchestrator reports inference-service scheduled on central-ai-pool; edge-ai-pool has available GPU capacity. Which action best addresses the likely cause?
Options:
A. Scale out the central training job pool
B. Constrain the inference service to the edge AI pool
C. Increase central object storage capacity
D. Enable PFC and ECN on the WAN path
Best answer: B
Explanation: Edge AI is used when data must be processed close to where it is generated, especially for low-latency decisions or limited tolerance for WAN dependency. Here, the edge node is healthy and has unused GPU capacity, while camera bursts traverse the WAN and the inference service is scheduled in the central pool. That placement creates unnecessary round-trip delay for a real-time inspection decision. The appropriate remediation is to place or constrain the inference workload on the edge AI pool so frames can be processed locally and only summaries, events, or selected data need to traverse the WAN. Network tuning may help a fabric, but it does not fix an incorrect workload placement for an edge use case.
Topic: AI Fundamentals and Applications
A team reports that a RAG assistant became slow after the vector corpus grew from 5 million to 50 million chunks. Nexus Dashboard shows no fabric congestion, and Intersight shows the inference GPUs are below 40% utilization during slow requests.
| Pipeline stage | p95 latency | Operational observation |
|---|---|---|
| Object-store document read | 45 ms | steady throughput, low disk wait |
| Embedding/vector search | 5,900 ms | high index CPU, cache misses increased |
| Prompt transfer to inference | 35 ms | no ECN marks or drops |
| LLM generation | 1,200 ms | GPU queue depth near zero |
What is the most likely performance constraint?
Options:
A. Storage access for source documents
B. Inference serving GPU capacity
C. Embedding retrieval in the vector index
D. Network congestion between retrieval and inference
Best answer: C
Explanation: In a RAG pipeline, latency can come from several stages: reading source content, retrieving similar embeddings, moving the augmented prompt, or generating the response. The evidence points to embedding retrieval because vector search accounts for most of the p95 latency and has supporting symptoms: high index CPU and increased cache misses after corpus growth. The storage read is fast and steady, the network path has no congestion indicators, and inference GPUs are underutilized with almost no queueing. The practical next step would be to validate vector database index sizing, sharding, cache capacity, and query performance rather than tuning the fabric or adding inference GPUs.
Topic: AI Fundamentals and Applications
A data center team is planning several AI POD deployments for RAG inference now and fine-tuning later. The operations team needs a shared way to visualize the planned AI infrastructure, map workload requirements to compute, network, and storage dependencies, and maintain visibility during rollout. Which design best maps to these requirements?
Options:
A. Use AI PODs only as the visibility and collaboration tool
B. Use Hyperfabric AI only as the operational planning layer
C. Use a model-training dashboard as the infrastructure design tool
D. Use Cisco AI Canvas as the planning and visibility workspace
Best answer: D
Explanation: Cisco AI Canvas fits requirements centered on AI infrastructure planning, visibility, and operations. In this scenario, the team is not only deploying a fabric or selecting servers; it needs a shared view that connects AI workload intent to infrastructure dependencies across compute, network, and storage. AI Canvas is the Cisco solution concept that supports that planning and operational visibility context for AI infrastructure rollouts.
Hyperfabric AI and AI PODs can be part of the resulting infrastructure, but they do not replace the need for a planning and visibility workspace. The key takeaway is to match AI Canvas to cross-domain planning and operational awareness, not to model training or a single infrastructure layer.
Topic: AI Fundamentals and Applications
An operations team expanded an on-premises generative AI training environment. Jobs confined to the original capacity meet the benchmark, but jobs that span the new capacity finish 30% slower and show more retries.
Operational observation:
| Area | Original capacity | Added capacity |
|---|---|---|
| Design basis | Cisco AI POD baseline | Individually selected components |
| Network/storage policies | Standardized | Locally customized |
| GPU server profile | Consistent | Mixed firmware and adapters |
What is the most likely remediation?
Options:
A. Increase the model checkpoint interval for all jobs
B. Deploy the expansion as a matching Cisco AI POD
C. Move the training dataset to cloud object storage
D. Disable orchestration across the original and new capacity
Best answer: B
Explanation: Cisco AI PODs are intended to provide repeatable infrastructure blocks for AI deployments. The symptom appears only when workloads span the original and added capacity, and the observation shows the new capacity was not deployed from the same validated baseline. Mixed server profiles, adapters, firmware, and policy customization can create inconsistent performance and retry behavior across a distributed AI job. The appropriate remediation is to make the expansion another standardized AI POD, rather than treating it as an ad hoc collection of compatible-looking components.
Disabling scheduling across capacity might hide the problem, but it does not restore the repeatable building-block model that AI PODs provide.
Topic: AI Fundamentals and Applications
A team deployed a chat-based generative AI service for internal developers. The serving requirement is 200 concurrent sessions, p95 time to first token below 800 ms, sustained generation of at least 40 tokens/sec per session, and error rate below 1%. Which operational evidence best proves the service is meeting the requirement?
Options:
A. GPU utilization averages 92% with no power or thermal alerts during peak usage.
B. Nexus Dashboard shows no fabric congestion or packet drops on the inference VLAN.
C. Production telemetry shows 620 ms p95 TTFT, 45 tokens/sec, and 0.4% errors at 200 sessions.
D. An offline benchmark shows 50 tokens/sec on one GPU with no concurrent users.
Best answer: C
Explanation: Generative AI serving performance is proven by operational telemetry that matches the actual serving requirements: concurrency, user-visible latency, generation rate, and errors. For chat services, time to first token affects perceived responsiveness, while tokens/sec affects streaming completion speed. Evidence from production or production-like load is stronger than component health or isolated benchmarks because it validates the complete serving path: application, model runtime, GPU capacity, network, and storage dependencies. Resource metrics such as GPU utilization or fabric congestion are useful supporting signals, but they do not prove the service is meeting the user-facing SLO.
Topic: AI Fundamentals and Applications
A data science team validated a generative model in development on a single Cisco UCS GPU server. The model is now moving to production serving for an internal API. The production requirements are low-latency inference during bursty demand, rolling model updates, GPU health visibility, and no additional training in this phase. Which infrastructure adjustment best maps to this transition?
Options:
A. Add high-throughput file storage for larger training datasets.
B. Deploy GPU-backed inference replicas with orchestration, load balancing, health checks, and telemetry.
C. Keep the single server and restrict API access with a firewall rule.
D. Upgrade to tighter GPU-to-GPU interconnect for distributed training.
Best answer: B
Explanation: Moving from development to production serving changes the infrastructure priority from experimentation to reliable inference delivery. The model needs to be packaged and run as a production service with GPU-aware scheduling, multiple inference replicas, load balancing, health checks, rolling updates, and telemetry for GPU and service health. These capabilities support bursty request patterns and operational visibility without changing the model-training workflow.
The key distinction is that production serving optimizes availability, latency, scaling, and operations for inference, while training-focused upgrades optimize model development throughput.
Topic: AI Fundamentals and Applications
A manufacturer is planning an AI use case for camera-based defect detection on several factory lines. The design must meet these requirements:
| Requirement | Detail |
|---|---|
| Inference latency | Under 50 ms near each line |
| Connectivity | Continue operating during WAN outages |
| Data handling | Keep raw images on-site |
| Model lifecycle | Periodically retrain centrally |
Which design best maps to these requirements?
Options:
A. Central training cluster only with no edge orchestration layer
B. Central cloud inference with all camera streams sent over the WAN
C. Edge GPU inference with local storage and central retraining sync
D. On-site CPU-only servers using shared remote object storage
Best answer: C
Explanation: This is an edge AI placement pattern. Real-time visual inspection has a strict latency target and must keep working when the WAN is unavailable, so inference should run close to the cameras on local GPU-capable compute. Because raw images must remain on-site, local storage or caching is needed for image retention and preprocessing. Central retraining can still be supported by synchronizing approved datasets, features, model artifacts, or updates between the factory and a central environment. Orchestration at the edge helps deploy and update inference services consistently across lines or sites. The key distinction is that inference placement is driven by latency and resiliency, while retraining can be centralized because it is less latency-sensitive.
Topic: AI Fundamentals and Applications
A hospital runs a RAG inference service for clinical notes. The service must keep source data in the hospital data center, provide predictable response time during clinic hours, and allow the infrastructure team to control maintenance windows.
Telemetry shows local GPU nodes are healthy and Nexus Dashboard reports no fabric congestion. During peak demand, orchestration events show jobs being sent to an external GPU provider, and audit logs show document embeddings leaving the data center. What is the most likely remediation?
Options:
A. Constrain the workload to the on-premises AI cluster
B. Enable ECN on the data center fabric
C. Move the vector database to the external provider
D. Increase object storage replication to the cloud
Best answer: A
Explanation: On-premises AI infrastructure is used when local control, predictable performance, and data-location requirements are primary constraints. In this scenario, the local GPUs and network are not showing failure symptoms. The decisive evidence is orchestration sending jobs to an external GPU provider and audit logs showing embeddings leaving the data center. The remediation is to keep the RAG workload and its data path on the on-premises AI cluster, with appropriate capacity controls or placement policies so peak demand does not trigger external spillover. Network congestion tuning would not address the data-location violation, and moving more data to the cloud worsens the mismatch.
Topic: AI Fundamentals and Applications
A financial services company is deploying a generative AI RAG platform for internal analysts. Source documents contain regulated customer data that must remain in the company-owned data center. The retrieval and inference workflow needs predictable low latency during trading hours, and the operations team requires direct control of GPU capacity, storage placement, and network change windows. Which infrastructure decision best fits these requirements?
Options:
A. Deploy the RAG platform on an on-premises AI cluster
B. Use a public cloud GPU service for all workloads
C. Run only edge inference nodes in branch offices
D. Use SaaS document AI with external data ingestion
Best answer: A
Explanation: On-premises AI infrastructure is the best fit when the organization must keep data in a known physical location, control the full infrastructure stack, and deliver predictable performance from dedicated local resources. In this scenario, regulated source documents cannot leave the company-owned data center, and the team needs direct control over GPU capacity, storage placement, network tuning, and change windows. A local AI cluster can place GPUs, high-performance storage, and the data fabric close to the protected datasets while avoiding dependency on external service latency or provider maintenance windows.
Cloud or SaaS options may scale quickly, but they do not meet the stated data-location and direct-control constraints. Edge-only deployment targets distributed low-latency inference, not a centralized regulated RAG platform with local data governance.
Topic: AI Fundamentals and Applications
An AI POD runs distributed model training overnight and a RAG chatbot during business hours on the same GPU nodes, fabric, and storage cluster. Users report RAG responses taking 12–15 seconds, while nightly training benchmarks remain normal. Which diagnostic conclusion is best supported?
| Observation | Current state |
|---|---|
| LLM inference GPU utilization | 35–40% |
| GPU fabric RoCE counters | No abnormal PFC or ECN events |
| Vector database query p95 | 2.8 seconds |
| Storage read latency for index files | Elevated during RAG traffic |
Options:
A. The inference GPUs are undersized for the model.
B. The training all-reduce path is congested.
C. The RAG retrieval path is storage-bound.
D. The orchestrator failed to schedule RAG pods.
Best answer: C
Explanation: RAG workloads often stress retrieval infrastructure before they stress the model-serving GPUs. A RAG request typically performs embedding, vector search, document retrieval, and then LLM inference. In this case, the slow component is the vector database and its backing storage for index reads. Low GPU utilization and normal RoCE congestion indicators make a distributed training-style fabric issue unlikely. Normal overnight training benchmarks also argue against a general GPU interconnect problem. The next useful validation would be correlating RAG p95 latency with vector database query latency, cache hit rate, and storage read latency during business traffic. The key distinction is that RAG performance depends heavily on low-latency retrieval, while model training more often exposes sustained GPU and east-west fabric bottlenecks.
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