The problem description was half-missing, which honestly made this more stressful than the actual algorithmic content.
Start by clarifying the problem: define what activation means, how dependencies affect activation, and what side effects are expected. Then model the system as a directed graph and simulate each request using appropriate traversal and state tracking. Finally, summarize the final state and discuss edge cases and optimizations.
Pro tip: Explicitly state your assumptions about dependency semantics (e.g., whether activation requires all dependencies to be active) and side effects (e.g., cascading activations). This shows you can handle ambiguity and design robust solutions.
Ask questions to understand what 'activation' means, how dependencies influence it, what side effects can occur, and what the initial state is. Confirm whether dependencies are prerequisites or just relationships.
Represent features as nodes and dependencies as directed edges in a graph. Choose appropriate data structures (e.g., adjacency list) and define state variables (e.g., active/inactive, pending).
For each activation request, check if prerequisites are met. If so, activate the feature and propagate side effects (e.g., activate dependents). Use BFS/DFS or topological ordering to handle cascades.
Consider cycles, already active features, failed activations, and concurrent requests. Discuss time/space complexity and possible optimizations like memoization or incremental updates.
After processing all requests, report the final state of each feature and any side effects. Walk through a small example to validate the approach and ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.