← Anthropic Interview Insights
The version I'd prepped for had messier edge cases, so seeing these simplified constraints was a relief.
Start by clarifying the requirements and edge cases, then design a data model that tracks workers, grants, and work sessions with timestamps. Focus on the event-driven nature: promotions are queued until the next office entry, and salary is computed only when a grant fully covers a session. Outline an algorithm that processes events in chronological order, handling overlaps without merging.
Pro tip: Emphasize the importance of defining clear invariants and handling edge cases like partial coverage and overlapping grants, as these are common pitfalls. Also, discuss how you would test the system with unit tests for each rule.
Ask questions to understand the exact behavior: What defines a work session? How are grants applied? What happens if multiple grants cover a session? Can promotions be scheduled in advance?
Define entities: Worker (with current role and pending promotion), Grant (with amount, start/end times), WorkSession (with start/end times). Consider using intervals and timestamps.
Process events in chronological order: office entry (apply pending promotion), work session start/end, grant creation. For each work session, determine if a single grant fully covers it; if so, pay salary based on worker's role at that time.
Since overlapping grants are not merged, each grant is considered independently. A work session is paid only if at least one grant fully covers it. Avoid double payment if multiple grants cover the same session.
Analyze time and space complexity, and propose test cases for edge scenarios like promotions during a session, grants that partially overlap, and multiple grants covering the same session.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.