Data Structures and Algorithms

DSA Interview Questions

Top Atlassian DSA Interview Questions & Solutions | 2025 Guide

Preparing for Atlassian’s Data Structures and Algorithms (DSA) interviews can feel overwhelming, but with the right strategy, you can crack them confidently. Whether you’re a recent graduate or an experienced developer, mastering DSA is key to landing a role at this tech giant. Sign up for free DSA resources to get started!

Understanding Atlassian’s DSA Interview Process

Atlassian’s interview process emphasizes problem-solving, algorithmic efficiency, and clean code. Engineers here solve complex scalability challenges daily, so your ability to optimize solutions matters.

Key Focus Areas in DSA Interviews

Problem-Solving Skills: Interviewers assess how you break down problems and approach edge cases.
Code Quality: Clean, modular code is a must.
Optimization: Time and space complexity analysis is critical.
Communication: Explain your thought process clearly.

A 2023 survey by Interview Kickstart found that 75% of Atlassian candidates face 2–3 DSA rounds, each lasting 45–60 minutes.

Evaluation Criteria Comparison

Criteria	Weightage	Example
Correctness	30%	Does the code handle all test cases?
Efficiency	40%	O(n) vs. O(n²) solutions
Readability	20%	Meaningful variable names
Communication	10%	Clarifying doubts upfront

Question 2: Find the Longest Palindromic Substring

Problem Statement

Given a string, return its longest palindromic substring.

Approach and Solution

Expand Around Center: Check odd/even-length palindromes.
Dynamic Programming: Store palindrome status in a 2D table.

Java Code:

				
					public String longestPalindrome(String s) {
    int maxLength = 0, start = 0;
    for (int i = 0; i < s.length(); i++) {
        int len1 = expand(s, i, i); // odd
        int len2 = expand(s, i, i + 1); // even
        int len = Math.max(len1, len2);
        if (len > maxLength) {
            maxLength = len;
            start = i - (len - 1) / 2;
        }
    }
    return s.substring(start, start + maxLength);
}

Optimization Tips:

Time Complexity: O(n²), Space Complexity: O(1).

Question 3: Check if a Binary Tree is a Valid BST

Problem Statement

Given the root of a binary tree, determine if it’s a valid Binary Search Tree (BST).

Approach and Solution

In-Order Traversal: Verify that the traversal result is sorted.
Recursive Min/Max Check: Track valid ranges for each node.

Java Code

				
					public boolean isValidBST(TreeNode root) {  
    return validate(root, Long.MIN_VALUE, Long.MAX_VALUE);  
}  

private boolean validate(TreeNode node, long min, long max) {  
    if (node == null) return true;  
    if (node.val <= min || node.val >= max) return false;  
    return validate(node.left, min, node.val) && validate(node.right, node.val, max);  
}

Optimization Tips

Avoid integer overflow by using Long types.
Time Complexity: O(n), Space Complexity: O(h) (height of tree).

Question 4: Design a Circular Queue

Problem Statement

Implement a circular queue with enQueue, deQueue, Front, Rear, and isEmpty operations.

Approach and Solution

Array-Based: Use a fixed-size array with front and rear pointers.
Linked List: Dynamically manage nodes (better for variable size).

				
					class CircularQueue:  
    def __init__(self, k):  
        self.queue = [None] * k  
        self.size = k  
        self.front = self.rear = -1  

    def enQueue(self, value):  
        if self.isFull(): return False  
        if self.isEmpty(): self.front = 0  
        self.rear = (self.rear + 1) % self.size  
        self.queue[self.rear] = value  
        return True

Optimization Tips

Handle edge cases like full/empty queues.
Time Complexity: O(1) for all operations.

Question 5: Minimum Window Substring

Problem Statement

Given two strings s and t, find the smallest substring in s that contains all characters of t.

Approach and Solution

Sliding Window: Use two pointers and a hash map to track character counts.
Expand/Shrink: Expand the right pointer until all characters are included, then shrink the left.

Java Code

				
					public String minWindow(String s, String t) {  
    int[] map = new int[128];  
    for (char c : t.toCharArray()) map[c]++;  
    int left = 0, right = 0, minLen = Integer.MAX_VALUE, count = t.length(), start = 0;  
    while (right < s.length()) {  
        if (map[s.charAt(right++)]-- > 0) count--;  
        while (count == 0) {  
            if (right - left < minLen) {  
                minLen = right - left;  
                start = left;  
            }  
            if (map[s.charAt(left++)]++ == 0) count++;  
        }  
    }  
    return minLen == Integer.MAX_VALUE ? "" : s.substring(start, start + minLen);  
}

Optimization Tips

Use array instead of HashMap for faster lookups.
Time Complexity: O(n), Space Complexity: O(k) (k = unique characters in t).

Question 6: Merge Intervals

Problem Statement

Given an array of intervals, merge all overlapping intervals.

Approach and Solution

Sort and Merge: Sort intervals by start time, then merge adjacent overlaps.

Python Code

				
					def merge(intervals):  
    intervals.sort(key=lambda x: x[0])  
    merged = []  
    for interval in intervals:  
        if not merged or merged[-1][1] < interval[0]:  
            merged.append(interval)  
        else:  
            merged[-1][1] = max(merged[-1][1], interval[1])  
    return merged

Optimization Tips

Sorting dominates time complexity.
Time Complexity: O(n log n), Space Complexity: O(n).

Question 7: Detect Cycle in a Directed Graph

Problem Statement

Given a directed graph, check if it contains a cycle.

Approach and Solution

DFS with Recursion Stack: Track visited nodes and recursion stack to detect back edges.
Topological Sorting: Kahn’s algorithm (BFS-based) for cycle detection.

Python Code

				
					def hasCycle(graph):  
    visited = [False] * len(graph)  
    recursion_stack = [False] * len(graph)  
    for node in range(len(graph)):  
        if not visited[node]:  
            if dfs(node, graph, visited, recursion_stack):  
                return True  
    return False  

def dfs(node, graph, visited, recursion_stack):  
    visited[node] = True  
    recursion_stack[node] = True  
    for neighbor in graph[node]:  
        if not visited[neighbor]:  
            if dfs(neighbor, graph, visited, recursion_stack):  
                return True  
        elif recursion_stack[neighbor]:  
            return True  
    recursion_stack[node] = False  
    return False

Optimization Tips

Time Complexity: O(V + E), Space Complexity: O(V).

Question 8: Implement LRU Cache

Problem Statement

Design an LRU (Least Recently Used) cache that supports get and put operations.

Approach and Solution

Hash Map + Doubly Linked List: Use a hash map for O(1) access and a list to track usage order.

Java Code

				
					class LRUCache {  
    class Node {  
        int key, val;  
        Node prev, next;  
    }  
    private Map<Integer, Node> cache = new HashMap<>();  
    private int capacity;  
    private Node head, tail;  

    public LRUCache(int capacity) {  
        this.capacity = capacity;  
        head.next = tail;  
        tail.prev = head;  
    }  

    public int get(int key) {  
        if (!cache.containsKey(key)) return -1;  
        Node node = cache.get(key);  
        moveToHead(node);  
        return node.val;  
    }  
}

Optimization Tips

Use LinkedHashMap in Java for simpler implementation.
Time Complexity: O(1) for both get and put.

Question 9: Maximum Subarray (Kadane’s Algorithm)

Problem Statement

Find the contiguous subarray with the largest sum.

Approach and Solution

Kadane’s Algorithm: Track current and maximum sums iteratively.

Python Code

				
					def maxSubArray(nums):  
    max_sum = current_sum = nums[0]  
    for num in nums[1:]:  
        current_sum = max(num, current_sum + num)  
        max_sum = max(max_sum, current_sum)  
    return max_sum

Optimization Tips

Handle all-negative arrays.
Time Complexity: O(n), Space Complexity: O(1).

Question 10: Validate Sudoku Board

Problem Statement

Determine if a 9×9 Sudoku board is valid (no duplicates in rows, columns, or 3×3 subgrids).

Approach and Solution

Hash Sets for Rows, Columns, Subgrids: Use sets to track seen numbers.

Java Code

				
					public boolean isValidSudoku(char[][] board) {  
    Set<String> seen = new HashSet<>();  
    for (int i = 0; i < 9; i++) {  
        for (int j = 0; j < 9; j++) {  
            char num = board[i][j];  
            if (num != '.') {  
                String rowKey = num + " in row " + i;  
                String colKey = num + " in col " + j;  
                String boxKey = num + " in box " + i/3 + "-" + j/3;  
                if (!seen.add(rowKey) || !seen.add(colKey) || !seen.add(boxKey)) return false;  
            }  
        }  
    }  
    return true;  
}

Optimization Tips

Single-pass solution using strings for keys.
Time Complexity: O(n²), Space Complexity: O(n²)

Additional Preparation Tips for Atlassian DSA Interviews

Coding Practice Platforms

LeetCode: 300+ company-tagged questions.
HackerRank: Atlassian-specific challenges.
CodeSignal: Mock interviews with real-time feedback.

Platform Comparison

Platform	Atlassian Questions	Difficulty Range
LeetCode	150+	Easy–Hard
HackerRank	50+	Medium–Hard
CodeSignal	30+	Medium

Mock Interviews and Time Management

Practice with tools like Pramp or Interviewing.io.
Allocate 15 minutes for problem-solving and 10 minutes for coding.

FAQs About Atlassian DSA Interviews

What Resources Are Best for Atlassian DSA Prep?

Focus on platforms like LeetCode and HackerRank, and review Atlassian’s engineering blog for insights. For structured learning, enroll in GetSDEReady’s DSA Course to master core concepts.

How Important Is Optimization in Atlassian Interviews?

Extremely! Atlassian prioritizes scalable solutions. Always discuss brute-force approaches first, then optimize. Learn optimization techniques via GetSDEReady’s System Design Course.

Should I Practice Recursion for Atlassian?

Yes! Questions on trees, graphs, and backtracking often require recursion. Strengthen your skills with GetSDEReady’s Web Development Course.

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Top Atlassian DSA Interview Questions & Solutions | 2025 Guide

Understanding Atlassian’s DSA Interview Process

Key Focus Areas in DSA Interviews

Evaluation Criteria Comparison

Top Atlassian DSA Interview Questions and Solutions

Question 1: Reverse a Linked List in Groups of K

Problem Statement

Approach and Solution

Question 2: Find the Longest Palindromic Substring

Problem Statement

Approach and Solution

Java Code:

Optimization Tips:

Question 3: Check if a Binary Tree is a Valid BST

Problem Statement

Approach and Solution

Java Code

Optimization Tips

Question 4: Design a Circular Queue

Problem Statement

Approach and Solution

Optimization Tips

Question 5: Minimum Window Substring

Problem Statement

Approach and Solution

Java Code

Optimization Tips

Question 6: Merge Intervals

Problem Statement

Approach and Solution

Python Code

Optimization Tips

Question 7: Detect Cycle in a Directed Graph

Problem Statement

Approach and Solution

Python Code

Optimization Tips

Question 8: Implement LRU Cache

Problem Statement

Approach and Solution

Java Code

Optimization Tips

Question 9: Maximum Subarray (Kadane’s Algorithm)

Problem Statement

Approach and Solution

Python Code

Optimization Tips

Question 10: Validate Sudoku Board

Problem Statement

Approach and Solution

Java Code

Optimization Tips

Additional Preparation Tips for Atlassian DSA Interviews

Coding Practice Platforms

Platform Comparison

Mock Interviews and Time Management

What Resources Are Best for Atlassian DSA Prep?

How Important Is Optimization in Atlassian Interviews?

Should I Practice Recursion for Atlassian?

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