AI Fundamentals and Applications

DCAI · 60 questions

• A city public-health bureau wants an assistant that answers clinic policy questions from the municipal knowledge base without retraining the foundation model each week. Which AI/ML workload pattern best fits this requirement?
• County 911 records analysts need grounded answers from the CAD policy corpus with citations, not free-form generation. Which workload design meets that need?
• A library consortium asks whether indexing ordinance PDFs for retrieval is a different workload than fine-tuning a generative model on those PDFs. How should the municipal AI architect distinguish them?
• Transit planners schedule a multi-day GPU job to update a traffic-prediction model from last year’s sensor dumps. Which workload type are they running?
• Public-health epidemiologists kick off distributed training of a wastewater surveillance model across the city’s GPU cluster. What infrastructure characteristic best matches this training workload?
• A housing authority wonders why last night’s model-update job needed far more fabric bandwidth than today’s scoring service. What explains the difference?
• Clinic kiosks call a local GPU service to classify chest X-ray triage images in under a second per request. Which workload type is this?
• 911 dispatch adds a voice-intent model that must answer in near real time during peak call load. Which requirement is most characteristic of this inference workload?
• A port authority scores arriving container images against a fixed trained model all day without updating weights. What workload is in use?
• City communications wants a generative model that drafts plain-language summaries of council meeting minutes. How does this differ from pure classification inference?
• Municipal courts explore generative drafting of routine notice templates under human review. What correctly classifies this on the DCAI workload map?
• A school district confuses chatbot answer generation with classic supervised image-classifier training. What distinction should the municipal architect emphasize?
• County IT maps stages from collecting 911 incident labels through model retirement for a dispatch AI project. Which statement best reflects the AI lifecycle at program level?
• Public-health leadership asks where data preparation sits relative to training and deployment in the AI lifecycle. What is the correct placement?
• A city auditor wants a checkpoint before production inference goes live after validation. Which lifecycle stage provides that gate?
• Transit ops notices model drift after road construction patterns change and asks which lifecycle phase handles refresh. What should they emphasize?
• Housing data stewards debate when labeled datasets enter the lifecycle versus when the model is monitored in production. How should they separate those phases?
• 911 program managers need a retire-and-replace plan when an old intent model is superseded. Which lifecycle concern does this address?
• A municipal CIO asks which lifecycle activities consume the GPU training cluster versus the inference farm. What mapping is most accurate?
• Courts IT documents experiment tracking before promoting a model into the shared inference pool. Which lifecycle contrast are they capturing?
• City public health wants AI that flags unusual clinic wait-time patterns for staffing—not a new foundation model research lab. Which use-case framing fits?
• A 911 center evaluates AI for call-type triage assistance under human oversight. What use case does this represent?
• A transit agency uses computer vision to estimate platform crowding from existing CCTV feeds. Which municipal AI use case is this?
• A municipal utility forecasts short-term load using historical meter and weather features. At a concept level, what should infrastructure planners expect?
• Libraries deploy a retrieval assistant over digitized local-history collections. Which use-case pattern matches?
• A municipal port authority wants AI that flags unusual container-movement patterns so security can prioritize screening lanes. Which AI use case does this primarily represent?
• A city adult-education program wants generative AI that drafts practice questions for teachers, with mandatory human review before anything reaches students. Which framing best matches this municipal AI use case?
• Housing inspectors upload thousands of code-violation photos each week and need models that reorder the queue so the worst cases surface first. What AI use case is this?
• A small town needs occasional multi-day GPU training runs but cannot justify owning a large GPU fleet year-round. Which AI infrastructure type best fits that need?
• County counsel requires certain 911 audio-derived features to remain in the municipal data center, while data science may use cloud GPUs for non-sensitive model experiments. Which infrastructure placement matches this policy?
• A public-health GPU cluster will run PHI-adjacent clinic triage inference that city policy says must remain inside the municipal data center. Which AI infrastructure type is required?
• Intersection traffic cameras need near-real-time vehicle detection, but backhaul to the civic data center is intermittent. Which AI infrastructure type best fits?
• An airport authority must keep baggage X-ray assist inference available during ISP outages to the public cloud. Which infrastructure trade-off should lead the decision?
• Municipal courts want on-premises RAG over sealed records and a separate cloud-hosted bot for public FAQ pages. Which infrastructure pattern is this?
• Water-treatment plants need pump anomaly detection on-site, while model training will run centrally in the city data center. Which pattern best describes this AI infrastructure design?
• A regional consortium shares one cloud tenant for joint model training, but each city must run production inference inside its own municipal data center. Which infrastructure pattern is this?
• Library accessibility kiosks may be offline for hours yet still need speech and vision aids for patrons. Which AI infrastructure type enables that?
• Finance proposes placing all sealed juvenile-court ML feature stores in a public cloud region with no residency controls. What should the municipal AI infrastructure judgment be?
• City DC architects worry distributed training may stall even after adding GPU servers. Which AI environment component should they inventory first as a likely bottleneck alongside compute?
• A public-health cluster adds multi-GPU servers and asks what NVLink provides compared with host PCIe alone. What is the primary purpose of NVLink in this AI compute stack?
• A 911 AI team must pack many inference microservices onto UCS hosts and debates VMs versus containers. What role do virtualization and containerization play in AI environments?
• A municipal platform team needs something that fairly places training jobs across GPU nodes and manages their lifecycle. Which AI environment component primarily provides that?
• Operations wants GPU utilization, fabric congestion, and storage latency visible together for the municipal AI stack. Which AI environment component are they asking for?
• Records-lake designers need fast scratch space for training checkpoints and separately shared datasets many jobs can read. How should they map block versus file storage roles?
• Sanitation analytics still relies on Fibre Channel SAN for high-throughput checkpoint volumes. What does this illustrate about AI storage components?
• Transit ML engineers need high IOPS for frequent model checkpoints during training. Which storage media class best matches that need in an AI environment?
• City architects draft a minimum inventory for a new municipal AI stack. Which set best reflects the Domain 1 component list for AI environments?
• A housing AI pilot under-provisions east-west bandwidth and then blames only the GPU SKUs for slow distributed training. What component lesson should the team apply?
• Clinic inference nodes want higher GPU utilization by running multiple services on shared accelerators. Which component approach primarily enables that density?
• County training jobs lose workers mid-run and operators refuse to SSH to every node to restart them by hand. Which AI environment component should provide resilient job lifecycle handling?
• After a silent drop on the municipal GPU fabric, county ops finds no alert fired and asks which AI-environment component should have been instrumented from day one?
• 911 lake architects place hot training corpora on NVMe and keep long-term CAD archives on capacity-oriented file storage. What AI-environment storage principle are they applying?
• A city CIO wants a pre-validated stack of UCS servers, GPUs, networking, and storage for municipal training instead of assembling parts ad hoc. Which Cisco AI solution matches that request?
• Public-health planners choose Cisco AI PODs so the civic DC can start epidemiology training sooner. What primary value does that choice emphasize?
• Municipal network, security, and IT operations teams want a Cisco-named AI solution where they and AI agents can jointly investigate, correlate, and resolve live issues across domains in one shared workspace. Which offering fits that requirement?
• During a cross-domain outage, a county operations team works the incident in Cisco AI Canvas alongside AI agents. What role does that solution play?
• A transit DC team evaluates a Cisco AI solution aimed at simplified AI-ready fabric operations for the GPU hall. Which named offering should they review?
• Ops contrasts Hyperfabric AI with a fully custom Nexus leaf-spine DIY build for the municipal GPU hall. When is Hyperfabric AI typically positioned?
• Procurement asks which Cisco-branded AI offerings the 300-640 DCAI blueprint names under Cisco AI solutions. Which set is correct?
• A city mistakes a generic hyperscaler marketplace GPU bundle for Cisco AI PODs. How should the architect correct that misunderstanding?