Building Blocks of AI

AIE · 48 questions

• A permitting office learns the difference between labeled and unlabeled plan-review examples before choosing a supervised classifier. What data role is being clarified?
• A 311 team cleans duplicate tickets and inconsistent street names before training a routing model. Why does that data-quality step matter?
• Public-health analysts distinguish structured lab-result tables from unstructured clinic free-text notes before feeding either into AI tools. What data-type literacy is shown?
• An assessor samples parcels fairly across neighborhoods so valuation models are not skewed to one ZIP code. What dataset practice is emphasized?
• A transit data steward maps ridership data origins from fareboxes, automatic passenger counters, and rider surveys. Why document those origins?
• A housing authority documents consent and retention rules before using case notes in any model. What data-governance building block is this?
• An open-data portal team explains AI data flow as collect → clean → train/evaluate → monitor in production. What literacy point are they teaching?
• Fleet managers hear that training an AI model means adjusting parameters on historical breakdown labels, not rewriting a long list of if-then rules. What development idea fits?
• A courts pilot separates training data (past ruling summaries) from evaluation data held out for testing. Why hold data out?
• A grants office chooses between fine-tuning a smaller model on city policy text and prompting a general LLM only. What adaptation literacy is in play?
• Emergency management tracks overfitting when a wildfire-risk model memorizes last year’s incidents but fails on new weather. What training risk is illustrated?
• Library IT explains inference as using a trained catalog model to answer a new patron query. How does that differ from training?
• A water utility version-controls which model build is live for leak alerts after retraining. What operations literacy does that show?
• An HR analytics team documents hyperparameters only at literacy level: settings that change how training behaves. What essentials idea fits?
• A tax office uses supervised learning to classify refund fraud risk from labeled historical cases. What machine-learning concept is demonstrated?
• Parks GIS clusters anonymous trail-counter patterns without predefined labels to find usage regimes. What learning approach fits?
• Traffic cameras feed a neural network that detects whether a crosswalk is occupied for a signal-priority pilot. What building-block idea fits?
• A 311 team compares a simple rules baseline to an ML model for urgency ranking and measures which helps residents more. What evaluation mindset fits?
• Public-health modelers describe deep learning as multi-layer neural nets useful for complex clinic image screening assists. What essentials definition fits?
• Utilities explain reinforcement-like control only at concept level for optimizing pump schedules with feedback from outcomes. What high-level idea fits?
• A city data academy teaches features versus labels using parking-citation examples for a basic classifier. What ML vocabulary idea is being taught?
• A clerk chatbot uses NLP to extract dates and case numbers from emailed continuance requests. What NLP capability is illustrated?
• A 311 team uses sentiment analysis to flag unusually angry resident messages for supervisor escalation—not automatic punishment. What careful NLP use is shown?
• A multilingual city portal applies machine translation then bilingual staff review for housing notices. What NLP practice supports equitable civic notices?
• Library search uses semantic similarity so “after-school STEM” matches related program descriptions. What NLP search idea is demonstrated?
• A council agenda tool summarizes hour-long hearing transcripts into bullet topics for staff. What NLP building block is in use?
• A benefits call center uses speech-to-text NLP to draft call notes for caseworkers. What speech-related NLP role fits?
• Policy analysts see named-entity recognition tagging ordinances, departments, and dollar amounts in PDFs. What NLP building block is shown?
• Communications drafts a recycling campaign with a large language model then fact-checks diversion rates. What GenAI literacy practice is essential?
• A housing chatbot grounded on lease FAQs still invents a fee; staff learn GenAI can produce plausible but false text. What risk is illustrated?
• A city attorney distinguishes GenAI creating new text from classical classifiers labeling existing tickets. What conceptual contrast is correct?
• A permitting team uses an LLM to rewrite dense code sections into plain language for applicants. What LLM use fits this building-block literacy?
• Parks marketing generates draft event images with a generative image model under brand guidelines. What GenAI literacy applies?
• IT explains tokens and context windows simply when a long ordinance paste gets truncated mid-answer. What LLM limit is being taught?
• Open-data educators show that LLMs predict likely next words—they do not “know” city policy like a clerk does. What conceptual model of LLMs is taught?
• Emergency ops explores multi-agent concepts where one assistant drafts alerts and another checks accessibility language. What advanced systems literacy fits?
• Transit studies computer vision plus LLM captioning to describe elevator outages for riders with visual impairments. What advanced systems idea is shown?
• A smart-city pilot discusses edge AI cameras that process locally to reduce raw video leaving intersections. What edge AI concept applies?
• An innovation office reviews retrieval-augmented and tool-using assistants that call the snow-emergency API. What advanced assistant pattern is under review?
• A health department hears about foundation models adapted to local clinic note styles under strict PHI controls. What advanced adaptation literacy fits?
• A city architect maps RPA bots plus GenAI drafting as a hybrid automation stack for invoice intake—not pure AI magic. What tool-selection literacy is shown?
• A small parks department chooses a simple FAQ search over a full custom LLM when budget and risk are low. What selection judgment is demonstrated?
• Courts pick a transcription service with strong accuracy on legal vocabulary rather than a generic consumer app. What tool-selection criterion is emphasized?
• Procurement scores vendors on data residency, audit logs, and human-review features—not flashy demos alone. What responsible selection approach is shown?
• A 311 team compares build versus buy for an intent classifier given staff skills and maintenance capacity. What selection judgment is tested?
• A public-health team rejects a free consumer chatbot for clinic triage because it lacks BAA and PHI safeguards. What tool-selection judgment is tested?
• A transit authority prefers a forecasting library their data scientists can explain to executives over an opaque black-box SaaS. What selection priority is shown?
• A records office picks OCR plus search first and treats generative rewrite as optional later for finding scanned deeds. What phased tool-selection idea fits?