Design and implement a GenAIOps infrastructure

AI-300 · 46 questions

• A city GenAI team is building a permitting chatbot and already has a classical Azure Machine Learning workspace. What should they create for GenAIOps?
• A county Foundry project must read municipal knowledge from Blob storage without embedding secrets in configs. What should they configure?
• A public-health GenAI app lets clinicians invoke agents but must stop them from changing foundation-model deployments. Which control applies?
• A transit authority handles rider-PII prompts through Foundry and must keep that traffic off the public internet. What network posture should they implement?
• A housing authority must provision Foundry infrastructure consistent with the city’s landing-zone standards. Which approach fits?
• A 311 program needs the same Foundry project layout in staging and production subscriptions. What scripting approach helps?
• A courts GenAI pilot must keep experimental prompts away from resident-facing production agents. What Foundry approach helps?
• A water utility needs operators and prompt engineers to have different Foundry permissions. What should platform admins do?
• A library consortium must meet the city security baseline that forbids public internet exposure on Foundry-dependent resources. What should they enforce?
• A parks chatbot platform proposes Bicep changes that add Foundry connections to municipal SharePoint knowledge bases. What review practice fits?
• A school-district GenAI lab wants classroom assistants under one governed Foundry resource without mixing every pilot into a single flat project. What structure helps?
• A fire-district assistant must call Azure AI Search grounding indexes without embedding secrets in the app. Which identity pattern fits?
• A city SRE must confirm Foundry-dependent model endpoints are reachable only through the private network before go-live. What should they validate?
• A clerk’s office wants Foundry provisioned by Azure CLI, then access controls applied as a separate governed step. How should the pipeline be staged?
• A municipal AI CoE must allocate Foundry cost across departments and keep inventories searchable. What resource hygiene helps?
• A county security team wants GenAI builders productive without granting blanket Owner on Foundry. What access approach fits?
• A permits chatbot sees spiky daytime traffic and quiet nights. Which foundation-model hosting choice fits variable load?
• A transit multimodal assistant must run a vision-capable foundation model on station camera stills with controlled compute. What deployment option fits better than pure bursty serverless chat?
• A public-health team needs a clinical FAQ assistant and must avoid oversized general chat models that overspend. What selection approach fits?
• A housing authority runs prompt evaluations that must target the exact foundation-model revision used in staging. What practice enables that?
• A 311 program expects a storm-driven surge and must keep answer latency inside SLA. Which capacity control helps?
• A courts summarization service has a new foundation-model version ready. What production deployment strategy fits?
• A water-utility ops copilot must generate SCADA query helpers. Which foundation-model selection fits better than a generic creative chat model?
• A library chat pilot is early and traffic is uncertain. How should they approach provisioned throughput?
• A parks recreation assistant must meet city policy that requires content-filter-compatible foundation-model settings. What should deployment include?
• A school-district tutoring bot must only serve a foundation-model version that already passed the district evaluation suite. What should operators do?
• A fire-district after-action report assistant finds an oversized foundation model that exceeds the city’s per-token budget. What should the team do?
• A city open-data narrator expects a council-meeting livestream Q&A spike. How should PTU capacity be handled?
• A clerk’s generative forms helper must redraft large overnight batches, but serverless quotas cannot meet the window. What hosting choice fits?
• A county GenAIOps board requires a canary week when updating foundation models. What deployment pattern satisfies that policy?
• A sanitation route-advice agent serves field workers on mobile devices where response delay hurts job flow. What model-selection priority fits?
• A municipal contact center wants reserved capacity for the production Foundry deployment while keeping lab experiments cheaper. What should GenAIOps do?
• A permits chatbot must stop inventing ordinance language and instead cite city code. What prompt-design practice helps most?
• A 311 assistant team wants to try concise versus detailed system prompts and compare how residents react. What should they do?
• A public-health FAQ bot must not push prompt edits straight to production Foundry. What control fits GenAIOps?
• A transit disruption bot’s diffs are messy because system instructions and few-shot examples live in one blob. What structuring practice helps?
• A housing eligibility helper must choose between two prompt variants before go-live. What comparison approach is sound?
• A courts clerk assistant may only run prompts that auditors can prove were approved. What Git practice supports that?
• A water-quality advisor must not invent medical advice when residents ask about illness after a boil-water notice. What prompt redesign helps?
• A parks event bot serves residents in several languages and must check whether prompt variants stay equally helpful. What should GenAIOps do?
• A school-district GenAIOps pipeline must stop production when Foundry prompts no longer match source control. What enforcement fits?
• A library research assistant must not paste entire copyrighted books into chat. What prompt-design addition helps?
• A fire-public-info bot has experimental prompt branches that must never reach the live Foundry deployment slot. What practice keeps them apart?
• A city grants office is comparing prompt variants that change how retrieval instructions are written. Which metric comparison is most appropriate?
• A clerk’s office needs auditors to understand who wrote each prompt and why it exists. What Git hygiene helps?
• A municipal AI board finds a clever prompt that strips required disclaimer language from resident legal questions. What should happen?