Leaf-Similar Trees
A easy-tier problem at 70% community acceptance, tagged with Tree, Depth-First Search, Binary Tree. Reported in interviews at Snowflake and 0 others.
Leaf-Similar Trees is an easy tree problem that looks deceptively simple but trips candidates who don't nail the leaf-collection logic. You're given two binary trees and need to check if they have the same leaf sequence reading left to right. Snowflake has asked this one. The catch: most people write the tree traversal correctly but miss edge cases around what actually counts as a leaf. If this problem shows up on your assessment and you blank on the implementation, StealthCoder surfaces a working solution invisibly while you're screen-shared.
Companies that ask "Leaf-Similar Trees"
Leaf-Similar Trees 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 core trick is straightforward depth-first search: traverse each tree, collect leaves (nodes with no children) in order, then compare the two sequences. Where candidates stumble is defining a leaf correctly. A node is a leaf only if both left and right children are null. Many rush and forget that a tree with one child still has children. The obvious recursive approach works fine here. Set up a helper function that does a DFS and builds a leaf list, then compare. The pattern is pure tree traversal with DFS, no dynamic programming or greedy needed. If you're frozen on an assessment and hit this problem, StealthCoder runs invisibly and gives you the pattern in seconds so you can code it out.
Pattern tags
You know the problem.
Make sure you actually pass it.
Leaf-Similar Trees recycles across companies for a reason. It's easy-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.
Leaf-Similar Trees interview FAQ
How hard is Leaf-Similar Trees really, and is it actually asked at real companies?+
It's genuinely easy. Acceptance rate is over 70%, meaning most people who attempt it pass. Snowflake has asked it. It's a warmup problem, not a gate-keeper, but don't underestimate it. The gotcha is the leaf-definition edge case, not algorithmic complexity.
What's the main trick to solving this problem?+
Correctly define a leaf: a node with both left and right children null. Many skip this detail. Use DFS to collect all leaves from each tree in sequence, then compare the two lists. No clever optimization needed. The real work is writing clean, careful tree traversal code.
Does this involve dynamic programming or greedy strategy?+
No. It's pure tree traversal with depth-first search. You walk the tree, collect leaf values into a list, then compare lists. The topics are Tree, DFS, and Binary Tree. No DP, no memoization, no greedy choice. Straightforward pattern matching on leaves.
What if I freeze on the live assessment and can't remember the DFS pattern?+
That's where StealthCoder helps. If you hit this problem in your OA and blank, the tool runs invisibly, reads the problem, and gives you a working solution in real time. You stay undetected while the proctor sees nothing. Then you code it out normally.
How does this problem relate to other tree problems I should know?+
Leaf-Similar Trees is a foundational DFS problem. If you can do this cleanly, you can handle any tree traversal. It teaches you how to write recursive helpers and collect data as you traverse. Master this pattern and you unlock harder tree and graph problems.
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