Artificial Intelligence Fundamentals

GOAA · 20 questions

• A city CISO asks the purple team whether a permitting FAQ assistant that retrieves ordinance PDFs is better modeled as retrieval-augmented generation or as a fine-tuned ticket classifier. Which choice best matches the design?
• A county fusion center compares generative adversarial networks used in deepfake pipelines with retrieval-augmented generation used in analyst assistants. Which statement best captures the contrast?
• A transit SOC evaluates NLP entity extraction on rider-complaint tickets against a custom GPT that answers from ops playbooks. Which distinction is most accurate?
• A water-utility red team debates ‘adversarial AI’ model-poisoning risk versus ‘offensive AI’ techniques used in authorized emulation. Which framing is correct for municipal purple-team language?
• A housing-authority security lead inventories commercial LLM APIs versus open-source local models for an isolated court-adjacent lab. Which comparison best fits constrained civic environments?
• A 311 program stores ordinance chunks in a vector database so an assistant can cite source passages during resident chats. What role does the vector database primarily play?
• A public-health IR desk builds a custom GPT that only answers from approved outbreak playbooks. What fundamental capability is being applied?
• A school-district SOC asks whether prompt-based summarization of phishing tickets is primarily NLP understanding of existing text or generative composition of new narrative. Which characterization is best?
• A parks IT team wonders if embedding search over trail-policy docs is the same as training a new foundation model. Which statement is correct?
• A municipal legal-tech pilot uses a GAN demo to illustrate synthetic ID-photo risk to clerk staff. Which AI-fundamentals concept is being taught?
• A city purple team labels an AI that helps craft authorized phishing lures as offensive AI, not merely ‘AI security automation.’ Why is that classification appropriate?
• A county auditor asks whether a vendor chatbot with no retrieval layer can still hallucinate ordinance numbers. Which fundamentals insight is most accurate?
• An emergency-management desk evaluates multimodal models that read handwritten shelter intake forms. Which AI-fundamentals consideration applies?
• A transit architecture review asks which AI platform pattern best supports private on-prem inference for an OT-adjacent security lab. Which answer fits?
• A court-clerk modernization team maps ‘assistant’ tools that call ticketing APIs versus pure chat summarizers. What distinction should they document?
• A city threat-intel unit explains that poisoning a model is adversarial ML while AI-written phishing is offensive AI tradecraft. Which statement reinforces that distinction?
• A library consortium prototypes RAG over public-meeting minutes and must explain chunking and embeddings to IT governance. Which description is most accurate?
• A municipal SOC compares token-prediction chat models with discriminative classifiers used for malware-family labels. Which contrast is correct?
• A county CIO asks which fundamentals topic covers why vector similarity search can surface ‘near’ but wrong policy passages in a civic RAG system. Which explanation is best?
• A purple-team charter requires staff to name NLP, GAN, RAG, vector databases, and custom GPTs before any deepfake lab. What is the charter enforcing?