The activate part was fine, just check all prereqs in the active set.
Model the feature dependencies as a directed graph and use topological ordering or DFS to manage activation and deactivation. For activation, ensure all prerequisites are active; for deactivation, perform a transitive cascade to deactivate dependents. Detect cycles during graph construction or before activation to prevent invalid states.
Pro tip: Discuss the trade-offs between eager and lazy cycle detection, and how you would handle concurrent operations to ensure thread safety in a distributed environment.
Ask clarifying questions about scale, concurrency, persistence, and whether features can have multiple prerequisites. Confirm that deactivation should cascade to all dependents transitively.
Represent features as nodes in a directed graph, with edges from prerequisite to dependent. Use adjacency lists for efficient traversal and maintain a set of active features.
For activate, check all prerequisites are active and no cycles exist. For deactivate, perform a BFS/DFS to find all transitive dependents and deactivate them. For cycle detection, use DFS with recursion stack or topological sort.
Address scenarios like activating an already active feature, deactivating a feature with no dependents, and concurrent requests. Use locks or transactional semantics to maintain consistency.
Discuss time and space complexity of operations, and trade-offs between eager vs lazy cycle detection, and between in-memory vs persistent storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.