I went straight to topological sort, which was the right instinct, but I fumbled the part where multiple prerequisites feed into one task.
Model the tasks as a directed acyclic graph (DAG) where edges represent dependencies. Use topological sorting to detect cycles and compute earliest finish times in linear time, then take the maximum finish time as the minimum total time.
Pro tip: Explicitly discuss how you would handle large-scale dependencies and parallel execution, as Netflix values scalability and efficiency in distributed systems.
Represent tasks as nodes and dependencies as directed edges. Clarify that a cycle indicates invalid dependencies.
Use Kahn's algorithm or DFS to detect cycles and produce a topological ordering of tasks.
Process tasks in topological order, setting each task's earliest start as the max finish time of its prerequisites, then add its duration.
The minimum total time is the maximum earliest finish time across all tasks, assuming unlimited parallelism.
Discuss O(V+E) time and space complexity, and handle edge cases like empty input, single task, or disconnected components.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.