Similar String Groups
A hard-tier problem at 55% community acceptance, tagged with Array, Hash Table, String. Reported in interviews at DoorDash and 0 others.
Similar String Groups is a hard problem that appears on DoorDash assessments and catches most candidates off-guard because it looks deceptively simple until you realize the grouping logic. The accept rate sits at 55 percent, which means nearly half the people who attempt it fail. The trap is trying to solve it with string comparison alone when you actually need to recognize this as a graph connectivity problem. Once you see it, the pattern clicks. Before that moment, you're stuck spinning on nested loops. If this problem hits your live assessment and you haven't drilled the union-find or DFS angle, StealthCoder surfaces the working solution in seconds, invisible to the proctor.
Companies that ask "Similar String Groups"
Similar String Groups 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe core insight is that two strings are 'similar' if you can transform one into the other by swapping exactly two characters. This isn't about raw string distance. You need to build a graph where each string is a node, connect nodes if the corresponding strings are similar, then count connected components. Most candidates try brute-force string matching first and get tangled. The efficient approach uses either union-find to merge similar strings into groups, or DFS/BFS to traverse the graph and count islands. Union-find scales better because you avoid explicit graph construction. The pitfall is off-by-one errors in the swap logic and forgetting that similarity is mutual (if A is similar to B, then B is similar to A). When your first draft times out or your grouping logic breaks on edge cases, StealthCoder gives you a clean, working implementation that handles the graph structure correctly.
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Similar String Groups 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Similar String Groups interview FAQ
Is Similar String Groups actually a DFS or union-find problem?+
It's fundamentally a graph connectivity problem, so both work. DFS counts connected components by traversing each unvisited node. Union-find groups similar strings efficiently without building an explicit graph. DFS is intuitive; union-find is faster. Both are valid. The problem tests whether you recognize the pattern shift from string manipulation to graph structure.
How do I check if two strings are similar without TLE?+
Iterate once, count positions where characters differ. If the count is exactly 2, check if swapping those two positions makes the strings equal. If count is 0, they're already identical and are in the same group. Early exit if diff count exceeds 2. This is O(n) per pair check and avoids redundant work.
What's the trick to avoid re-checking pairs?+
Once you mark two strings as connected (via union or visited flag in DFS), you don't process that pair again. If using union-find, a string only initiates new comparisons from its current index forward. This prevents double work and keeps complexity manageable even on larger inputs.
Does DoorDash ask this differently than standard LeetCode?+
DoorDash's version typically sticks to the core problem: group strings by similarity and return the count. No exotic twists reported. The focus is on whether you recognize graph connectivity under time pressure. The accept rate of 55 percent suggests time constraints and pattern blindness are the main failure modes.
Should I use union-find or DFS if I'm not sure which is faster?+
Union-find edges out DFS slightly on large inputs because it avoids graph construction overhead. DFS is more familiar to most candidates and easier to code correctly under pressure. If you're comfortable with union-find, use it. If DFS feels safer, go with that. Both pass within reasonable time limits on the expected input sizes.
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