AI Foundations and Technology Ecosystem

CRAGE · 20 questions

• A city CIO tells council that a 311 triage tool qualifies as AI. What criterion should the CIO emphasize?
• Public-works analysts want to classify pothole severity from labeled inspection photos and separately discover themes in unlabeled work-order notes. Which pairing correctly maps the learning approaches?
• A transit authority contrasts a fare-discount engine that applies fixed eligibility rules with a GenAI assistant that drafts rider-service replies. What distinction should governance emphasize?
• A county assessor briefs the board on how AI used for deed summarization evolved over decades. Which narrative best reflects that evolution for civic governance literacy?
• A housing authority compares two AI uses: scoring tabular eligibility fields and analyzing inspection photos with deep learning vision. What distinction should staff document?
• Emergency management reviews a flood-alert tool that chains multi-step actions versus a single static flood-risk classifier. What capability should leaders associate with the chained system?
• Library digital services maps three AI uses across modalities. Which mapping best illustrates real-world civic AI applications by type?
• A municipal open-data team shows that a permitting chatbot, a traffic-signal optimizer, and a benefits fraud-flag model share a common pattern. What pattern should governance recognize?
• A water utility diagrams governable stages for a leak-prediction project. Which sequence correctly reflects a typical AI project life cycle?
• A city MLOps lead briefs executives on practices that make civic models governable. Which set best captures governance-visible MLOps practices?
• A county DataOps team sets prerequisites before any resident-facing model ships. Which prerequisite best reflects DataOps’ role in trustworthy municipal AI?
• A public hospital district compares deployment options for a PHI-heavy triage assistant and a non-PHI drafting helper. Which deployment approach best matches governance needs?
• 311 product owners inventory components of their AI technology stack for governance. Which inventory best covers the stack layers leaders must track?
• A parks department chooses where litter-detection inference will run for trail cameras. What tradeoff should leaders weigh between edge and central cloud batch scoring?
• Courts IT documents system components for the AI inventory. How should foundation-model APIs, fine-tuned local classifiers, and classical models be treated?
• Fire/EMS contrasts nightly station risk scoring with streaming call-type prediction during active dispatch. Which statement correctly distinguishes the inference modes?
• An elections office compares two ways to help clerks answer procedure questions about municipal code. What contrast should governance understand between RAG and fine-tuning?
• An assessor’s office prepares to approve a valuation-assist model. Which artifacts most directly support governable documentation of how the AI system was built?
• City digital services explains why a code-enforcement intake that accepts text plus photos differs from a text-only chatbot. What capability is being highlighted?
• A utility cybersecurity board asks how to separate accountabilities in an AI outage predictor. Which separation best supports clear governance?