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Tree Algorithms

Tree algorithms interview questions

Binary trees, BSTs, tries, DP-on-trees. The shape that shows up everywhere — file systems, DOM, expression parsers, search structures. Master the four traversals and tree-recursion template, and 80% of tree interview problems become mechanical.

Four traversals — DFS pre/in/post + BFS level-order

Pre-order: root → left → right. In-order: left → root → right (sorted for BST). Post-order: left → right → root (used for delete/aggregate). Level-order: BFS, queue-based.

Tree-recursion template

Solve children first; combine at the root. Most tree problems fit this template: recurse left, recurse right, do something with the results at the current node.

Binary search trees (BSTs)

In-order traversal yields sorted output. Insert, search, delete all O(log n) when balanced. Self-balancing variants (AVL, red-black) usually NOT asked in interviews — but BST invariants are.

Tries — prefix-tree for strings

Each node is a character; root-to-leaf path is a word. Used for: autocomplete, word search, prefix matching, IP routing. Often the cleanest solution for ‘word starts with’ problems.

DP on trees — the upgrade to tree recursion

Compute a value per node based on subtree results. Diameter of tree, robber on tree, max-sum path. The recursion returns multiple values per node (state + answer).

Voice-paced practice with recursion clarity grading

Tree problems are won on recursion clarity — being able to articulate ‘what each call returns’. The AI coach grades that articulation explicitly.

The four traversals

The four tree traversals and when each one wins

Pre-order (root → left → right) — Used for: tree serialization, copying a tree, expression-tree evaluation in prefix notation.

In-order (left → root → right) — Used for: BST sorted output, validating a BST, kth smallest in BST.

Post-order (left → right → root) — Used for: deletion (delete children first), tree height, DP-on-trees, expression-tree evaluation in postfix.

Level-order (BFS, queue) — Used for: zigzag traversal, level averages, right side view, vertical order traversal.

The tree-recursion template (memorize this once)

Most tree problems fit one pattern. Learn this template once; reuse forever.

function solve(node):
  if node is None:
    return base_case

  left_result = solve(node.left)
  right_result = solve(node.right)

  # combine left, right, and node.val
  return combined_result

Almost every tree problem boils down to: define base_case, define how left_result and right_result combine with node.val. Diameter, depth, balanced-check, path-sum, lowest-common-ancestor — all variations of this template.

  • Variation 1: Return multiple values. Some problems need both an answer AND a state (e.g., is-balanced + height). Return a tuple.
  • Variation 2: Use a class-level result. For problems like “diameter”, you update a global max while recursing.
  • Variation 3: Pass state down. Some problems pass cumulative state (e.g., path-sum) as a parameter.

The 8 most common tree interview questions

  1. Maximum depth of binary tree — Basic recursion template warm-up
  2. Validate binary search tree — In-order traversal yields sorted; equivalently pass min/max bounds
  3. Lowest common ancestor (LCA) — Two variants: BST and general binary tree
  4. Serialize and deserialize binary tree — Pre-order with null markers
  5. Diameter of binary tree — DP-on-trees with global max
  6. Path sum (II — return all paths) — DFS with backtracking
  7. Construct binary tree from preorder + inorder — Recursion with hashmap for O(n)
  8. Binary tree right side view — Level-order BFS, take last per level

Tries — when the cleanest solution is a tree of characters

Trie problems are usually disguised as string-search problems. Recognition cue: ‘starts with’, ‘prefix’, ‘autocomplete’, ‘word search in a board’.

  • Implement Trie (insert, search, startsWith). Canonical problem.
  • Word search II. Trie + DFS on the board. Brute-force is too slow.
  • Replace words. Trie of dictionary roots; walk each input word until you hit a root.
  • Longest word in dictionary. Trie + BFS or DFS, taking longest path with all prefixes present.

Frequently asked questions

How do I get better at tree interview questions?

Master the four traversals (pre/in/post/level-order) and the tree-recursion template. Once those are reflex, most tree problems fall into a small number of variations. Drill voice-paced — being able to articulate 'what each recursive call returns' is what wins tree interview rounds.

Should I use recursion or iteration for tree problems?

Recursion for almost everything. Iterative versions of in-order/pre-order/post-order using a stack come up occasionally — usually as a follow-up question after you've solved the recursive version. Iterative is harder to write under pressure; show the recursive solution first.

What's the difference between a BST and a binary tree in interviews?

Binary tree: any tree with at most 2 children per node. BST: a binary tree where left < node < right. In-order traversal of a BST yields sorted output — that property unlocks many BST-specific problems (kth smallest, validate BST, search). General binary tree problems don't have that ordering shortcut.

How important are tries for FAANG interviews?

Important enough to know cold. ~10% of FAANG coding rounds include a trie problem. The 'implement Trie' problem is a near-certain ask if interviewing at Meta. Memorize the implementation; recognize the 'prefix' cue.

Can AI tools help with tree problem practice?

Yes. PhantomCode's coach grades recursion clarity — can you articulate what each recursive call returns and how the parent combines results? That articulation is the single highest-leverage skill in tree interview rounds, and it doesn't show up in correctness alone.

Drill tree algorithms with recursion-clarity grading

Voice-paced practice with explicit recursion-articulation feedback. Same coach that runs in your real coding interview.

Start tree drills

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