A ferry check-in story says “fast check-in for regulars” but never defines who counts as a “regular.” Which LLM use best matches requirements-analysis support?
Select an answer to reveal the explanation.
Short Explanation
“Regulars” without a rule is a fog bank in the requirements—perfect for analysis help, not for inventing fares as truth. Ask the model to spot the gap and draft clarifying questions. Keep fiction out of the approved basis until people answer.
Full Explanation
LLM support for requirements analysis includes flagging ambiguity and drafting clarifying questions—exactly right when a ferry check-in story says “fast check-in for regulars” but never defines who counts as a regular. Civic ferry rules affect fares, queues, and equity; undefined personas are fog in the basis, not an invitation to invent policy and treat it as approved fact.
Inventing a complete fare table and treating it as approved product fact without asking stakeholders invents business rules and bypasses governance. Classifying overnight regression log severity buckets instead of reading the story addresses result analysis, not requirements cleanup. Generating production SQL migrations that enforce an assumed regular-traveler rule jumps to implementation on fiction and risks live ferry systems.
Exam caveat: analysis help spots gaps and proposes questions; it does not silently author approved civic business rules or production schema changes.
Operational check: prompt for ambiguities and clarifying questions on “regular”; send the questions to operations and policy stakeholders; update the story only after answers land; block any GenAI-invented fare table or SQL from entering the ferry test basis or release pipeline.