Minimize Malware Spread
A hard-tier problem at 42% community acceptance, tagged with Array, Hash Table, Depth-First Search. Reported in interviews at Dropbox and 2 others.
Minimize Malware Spread shows up in live OAs at Dropbox, DoorDash, and TikTok. It's a graph problem disguised as a network simulation, and the acceptance rate sits at 42 percent. Most candidates see it as a straightforward BFS or DFS traversal and miss the actual trick: you're not just finding infected nodes, you're optimizing which node to remove to minimize spread. The problem punishes greedy thinking. If you hit this during your assessment and blank on the containment strategy, StealthCoder surfaces a working solution invisible to the proctor.
Companies that ask "Minimize Malware Spread"
Minimize Malware Spread is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderThe trap is thinking this is only a pathfinding problem. You'll likely start with BFS or DFS to count reachable nodes from each potential starting point, which is correct, but the optimization layer is what separates pass from fail. You need to either iterate through candidates and simulate the removal, or use Union Find to precompute connected components. The graph structure matters: if the network is sparse, BFS per node is viable; if dense, you need Union Find or DFS with memoization to avoid timeout. Hash tables store your adjacency list and visited state. Most solutions require you to simulate removing each node and recount reachable malware, which feels brute-force until you realize the constraints allow it. StealthCoder solves this in real time if the pattern doesn't click during the live assessment.
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Minimize Malware Spread recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimize Malware Spread interview FAQ
Is this a pure graph traversal or optimization problem?+
Both. You'll traverse the graph (BFS/DFS) multiple times, once per candidate node you want to remove. The optimization is choosing which node to remove to minimize spread. If you only write traversal code without the removal simulation loop, you'll fail test cases. The problem forces you to combine exploration with strategic decision-making.
Why is the acceptance rate only 42 percent?+
Candidates often miss that you must simulate removal of each node, not just count infected nodes from a starting point. They also underestimate the hash table setup needed to store adjacency lists and track visited state across multiple simulations. Time complexity bugs are common when traversal runs are not properly optimized.
Should I use BFS, DFS, or Union Find?+
All three can work depending on constraints. BFS is straightforward per simulation. DFS is recursive and intuitive. Union Find precomputes components and is fastest if N is large. In an OA, start with BFS or DFS and a hash table for adjacency. If you time out, pivot to Union Find. DFS + memoization is usually the middle ground.
What's the common pitfall with graph setup?+
Forgetting that the graph may be undirected or directed. If edges are bidirectional, your adjacency list must reflect that, or your traversal will miss nodes. Also, not handling the initial node correctly: it's the malware source, not necessarily node 0. Hash table keys must match the node IDs in the input.
Does Dropbox, DoorDash, or TikTok ask this differently?+
Problem structure is consistent across reports, but the emphasis may vary. All three test your ability to combine graph traversal with optimization under time pressure. The core trick, simulating removal and recounting, is universal. Your solution must handle arbitrary graph topology and node counts without timing out.
Want the actual problem statement? View "Minimize Malware Spread" on LeetCode →