How many questions are in this quiz?

This quiz has 109 questions on tree traversals and heap properties.

What is the format of the questions?

Each question has 4 options and there is no timer, so you can work at your own pace.

Can I choose the number of questions and difficulty?

Yes. Before you start, select your preferred question count and difficulty to match quick practice or a longer session.

Does a heap also satisfy the binary search tree property?

No. A heap only guarantees parent-child ordering (min or max), not left-vs-right ordering like a BST.

Which mistakes does this quiz help catch?

It targets common errors like mixing traversal orders, misusing heap index formulas, and assuming heaps are fully sorted.

Tree Traversals & Heap Properties Quiz

About this quiz

What you’ll practice

Move confidently between preorder, inorder, postorder, and level-order traversals, and translate them into practical reasoning about recursion and queues. You’ll also test core heap rules (min-heap/max-heap), array indexing, and parent/child relationships.

Each question uses 4 options and there’s no timer, so you can slow down to validate invariants, trace steps, and catch off-by-one mistakes. Choose your question count and difficulty before starting to tailor the session to quick revision or deeper practice.

Common pitfalls to avoid

Many wrong answers come from mixing traversal outputs (especially inorder vs. preorder) or forgetting how level-order is produced with a queue. With heaps, frequent slips include incorrect 0-based vs 1-based index formulas and assuming a heap is also a BST.

Confusing traversal names with their visit order (NLR/LNR/LRN)
Misapplying heap property to all subtrees without checking parent-child comparisons
Treating heaps like sorted arrays or binary search trees
Missing edge cases: single node, skewed tree, duplicates, empty children
Mixing 0-based and 1-based heap index calculations

How difficulty is balanced

Difficulty is mixed by design: easier items reinforce definitions and basic traces, while tougher ones combine traversal reconstruction, heapify steps, and complexity reasoning. This blend helps you build momentum early and still get meaningful challenge as you continue.