Questions
21 of 26
1What is a Tree? Define Node, Root, Leaf, Edge, Height, Depth.
2What is a Binary Tree?
3What is a Binary Search Tree (BST)? What are its properties?
4What is the difference between a Binary Tree and a Binary Search Tree?
5Explain Tree Traversals: Inorder, Preorder, Postorder, Level Order.
6How do you implement Inorder traversal iteratively? (Without recursion)
7How do you find the Lowest Common Ancestor (LCA) of two nodes in a BST?
8How do you validate if a given Tree is a valid BST?
9What is a Balanced Tree? Why do we need balance?
10What is a Complete Binary Tree? A Full Binary Tree? A Perfect Binary Tree?
11What is a Heap? (Min-Heap and Max-Heap)
12How is a Heap implemented using an Array? (Parent/Child index math)
13What is the time complexity of insertion and deletion in a Heap? Why?
14What is Heapify? Explain the process.
15What is a Trie (Prefix Tree)? When is it used?
16How does a Trie compare to a Hash Table for string storage?
17What is an AVL Tree? What is a Red-Black Tree? What is the difference?
18Why do databases prefer B-Trees/B+ Trees over Binary Search Trees?
19How do you serialize and deserialize a Binary Tree?
20What is a Segment Tree? What problems does it solve?
21What is a Fenwick Tree (Binary Indexed Tree)?
22What is the difference between a B-Tree and a B+ Tree? Why are B+ Trees better for disk access?
23Explain the concept of Tree Rotation.
24What is a Splay Tree? When would you use it?
25How do you find the diameter of a Binary Tree?
26How do you check if a tree is symmetric (Mirror image)?
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What is a Fenwick Tree (Binary Indexed Tree)?

Difficulty: 6/10
prefix sum, point update, logarithmic time

Fenwick Tree

A Fenwick Tree, or Binary Indexed Tree, is a compact structure for maintaining prefix aggregates and supporting point updates efficiently. It uses the binary representation of indices to store partial sums. For an array of n elements, point updates and prefix-sum queries both take O(log n), while memory usage is O(n).

javascript
  1. 1

    Point update: O(log n).

  2. 2

    Prefix query: O(log n).

  3. 3

    Range sum: O(log n) using two prefix queries.

  4. 4

    Space: O(n).

  5. 5

    Simpler and often more memory-efficient than a Segment Tree for suitable operations.

  6. 6

    It is primarily suited to operations with an invertible prefix aggregation such as sum.

Scenario Questions

0-2 years experience

  1. 1We have an array of daily sales numbers, and we need to support queries like 'total sales from day i to day j' and updates to a single day's sales. How would you use a Fenwick Tree to implement this?
  2. 2If you built a Fenwick Tree for 1‑based indexing and then tried to query the prefix sum for index 0, what would happen and why?
  3. 3Can you walk me through the steps to update the value at position 5 by +3 in a Fenwick Tree of size 10?

2-5 years experience

  1. 1Our leaderboard service stores scores and needs to return the rank of a user after each score update. We tried a Fenwick Tree but observed occasional off‑by‑one errors when users have the same score. What could be causing this and how would you fix it?
  2. 2During a recent release we switched from a segment tree to a Fenwick Tree to reduce memory usage, but the latency for range sum queries doubled for some large ranges. Explain why this might happen and what trade‑offs you would consider.
  3. 3You need to support both prefix sum and point assignment (set value) operations. How would you adapt a Fenwick Tree to handle assignments efficiently?

5-8 years experience

  1. 1Design a high‑throughput analytics pipeline that ingests millions of events per second and needs to answer real‑time cumulative metrics over sliding windows. How would you incorporate a Fenwick Tree, and what limitations would you need to address at scale?
  2. 2Our distributed cache stores partial Fenwick Trees per shard, and we need to combine them for a global query. Describe an algorithm to merge results and discuss consistency concerns.
  3. 3Explain how you would modify a Fenwick Tree to support range updates and range queries (i.e., a BIT of BITs) and evaluate its performance compared to a segment tree in a multi‑threaded environment.

8+ years experience

  1. 1We have a legacy codebase that uses a custom prefix‑sum array for reporting, and we’re considering migrating to a Fenwick Tree across multiple services. What architectural considerations, testing strategies, and migration plan would you propose to minimize risk?
  2. 2When designing a new data‑processing framework that must be extensible for future query types (e.g., percentile, top‑k), how would you decide whether to expose a Fenwick Tree as a core primitive or abstract it behind a higher‑level API?
  3. 3Discuss the long‑term maintenance implications of embedding Fenwick Trees in a microservice that also needs to support rollbacks and versioned data schemas.

Follow-up Questions

  • What would be the impact of using 0‑based indexing instead of 1‑based?
  • How does the memory usage compare to a segment tree for the same data size?
  • Can you extend the BIT to support range updates, and what changes are required?
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