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Verkada·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Verkada SWE interview that went deep into graph traversal pretty fast. The core problem was about access control across nested group structures and it turned into a full design exercise with complexity analysis and edge case discussion.

Questions Asked (1)

Q1

Given a system where users can own cameras directly or belong to groups that own cameras, and groups can be nested inside other groups arbitrarily, design an algorithm to find all admin users who have access to every camera. Cover your input representation, how you handle cycles in group membership, a BFS-based traversal approach, and time/space complexity.

Algorithms & Data StructuresSystem DesignData Modeling
Author's notes

This one took me a minute to even set up properly.

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AI HintsAI Generated

Suggested Approach

Model the ownership hierarchy as a directed graph and use BFS from each camera to find all users with access, then intersect the sets to find admins with access to every camera. Alternatively, reverse the graph and propagate access from users to cameras, tracking visited groups to handle cycles. Clearly state assumptions about admin privileges and input representation before diving into the algorithm.

Pro tip: Mention that in production systems like Verkada's, you'd likely precompute and cache access lists or use a graph database to avoid repeated traversals, and discuss trade-offs between eager and lazy evaluation.

1. Clarify input representation and assumptions

Define the data structures: users, groups, cameras, and relationships (user->camera, user->group, group->camera, group->group). Clarify what 'admin user' means and whether admins have implicit access.

2. Choose traversal strategy and handle cycles

Decide between forward (user to camera) or backward (camera to user) traversal. Use a visited set to avoid infinite loops in cyclic group memberships.

3. Implement BFS to collect access sets

For each camera, perform BFS from the camera node following reverse edges to find all users with access. Collect these users into a set per camera.

4. Intersect sets to find admins with access to all cameras

Compute the intersection of all per-camera user sets, then filter to only admin users. If any camera has no users, the result is empty.

5. Analyze complexity and discuss optimizations

State time complexity O(C * (V + E)) for C cameras, and space O(V + E) for the graph plus O(U) for sets. Mention optimizations like early termination or bitsets.

Key Points to Mention

  • Graph modeling: nodes for users, groups, cameras; directed edges for ownership and membership.
  • Cycle handling: use a visited set during BFS/DFS to prevent infinite loops.
  • BFS traversal: start from each camera and traverse reverse edges to find users, or start from users and propagate access.
  • Set intersection: use hash sets for efficient intersection of user sets across cameras.
  • Time and space complexity: O(C * (V + E)) time, O(V + E) space, where C is number of cameras.
  • Scalability considerations: precomputation, caching, or graph databases for large systems.

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