Perplexity·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Perplexity software engineer round that was basically one meaty coding problem about a task manager with dependency cascading. The graph traversal angle is where it gets interesting and where I think a lot of people stumble.

Questions Asked (1)

Design and implement a Python in-memory task manager for an AI workflow system. Tasks have unique IDs and statuses, and can depend on other tasks. You need to support adding tasks, adding dependencies between tasks, failing a task (which must cascade to all downstream dependents), and querying a task's status. The implementation must handle large graphs efficiently without redundant traversal.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with a straightforward DFS for the cascade and it felt clean until they mentioned the efficiency constraint.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a graph-based design using adjacency lists for dependencies and reverse adjacency for dependents. Implement operations with efficient data structures (hash maps, sets) and ensure cascade failure uses BFS/DFS with visited tracking to avoid redundant traversal. Discuss trade-offs and test with large graphs.

Pro tip: Mention that you would use a reverse dependency graph to efficiently find downstream tasks, and emphasize that marking tasks as failed only once prevents redundant traversal in large graphs.

1. Clarify Requirements and Constraints

Ask about expected scale, concurrency needs, and whether dependencies can be added after task creation. Confirm that failure cascades to all downstream dependents and that status queries should be O(1).

2. Design Data Structures

Propose using a dictionary for tasks (ID -> Task object) and two adjacency lists: one for dependencies (task -> list of dependencies) and one for dependents (task -> list of tasks that depend on it). Use sets for O(1) lookups and to avoid duplicates.

3. Implement Core Operations

Implement add_task (create task with status PENDING), add_dependency (update both adjacency lists, check for cycles if needed), fail_task (BFS/DFS over dependents, mark each as FAILED once, skip already failed), and get_status (return status from task object).

4. Optimize for Large Graphs

Ensure cascade failure uses an iterative BFS with a queue and a visited set to avoid recursion depth issues and redundant traversal. Only enqueue tasks that are not already failed.

5. Discuss Trade-offs and Testing

Talk about time/space complexity: O(1) for add and query, O(V+E) for failure cascade. Mention potential cycle detection and how to handle it. Suggest testing with large random graphs and edge cases like failing a task with no dependents.

Key Points to Mention

Use of reverse adjacency list (dependents graph) for efficient cascade failure
Visited set or status check to prevent redundant traversal and infinite loops
Time complexity: O(1) for add_task, add_dependency, get_status; O(V+E) for fail_task
Space complexity: O(V+E) for storing graph
Handling cycles: either prevent them during add_dependency or detect during failure cascade
Thread-safety considerations if concurrent access is required

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.