DSA Interview Questions & Blind-75 Tutorial
Master Data Structures & Algorithms with the definitive guide to the Blind-75 list. Comprehensive explanations, visual diagrams, and coding solutions for FAANG interviews.
Why DSA Matters for Interviews
Data Structures and Algorithms form the foundation of technical interviews at top tech companies like Google, Amazon, Facebook, Apple, and Netflix (FAANG). The Blind-75 list, curated from LeetCode, represents the most frequently asked questions in coding interviews.
Mastering these 75 problems doesn’t just mean memorizing solutions—it means understanding the underlying patterns and techniques that apply to hundreds of similar problems. This systematic approach is what separates successful candidates from the rest.
Companies don’t just test whether you can solve a problem—they evaluate your problem-solving process, communication skills, and ability to write clean, efficient code under pressure.
Covers Core Patterns
Learn 15+ essential problem-solving patterns used in 80% of interview questions.
Time & Space Complexity
Understand how to analyze and optimize your solutions for efficiency.
Multiple Languages
Solutions provided in Java, Python, and JavaScript for broader understanding.
Thinking Process
Step-by-step breakdowns of how to approach each problem type.
Blind-75: The Ultimate Interview Prep List
Curated based on actual interview frequency at top tech companies. Mastering these 75 problems gives you coverage for 90% of DSA interview questions.
Don’t try to memorize solutions. Focus on understanding patterns. Solve each problem, then identify 2-3 similar problems to reinforce the pattern. Aim for 2-3 problems per day for a 4-week mastery plan.
Blind-75 Problem Categories
Most fundamental category covering two-pointer technique, sliding window, and prefix sum patterns.
Palindrome checks, anagrams, subsequences, and string manipulation techniques.
Binary trees, BST, traversals, DFS/BFS, and graph algorithms for interview success.
Master memoization, tabulation, and state transitions for optimal solution design.
Arrays & Hashing
Most frequent interview questions category
Why Arrays & Hashing Matter
Arrays are the most fundamental data structure, and hashing (HashMaps/Dictionaries) provides O(1) average time complexity for lookups. Combined, they solve a vast majority of interview problems efficiently.
Key Techniques
- Two-pointer technique: Solving problems with sorted arrays or linked lists
- Sliding window: Finding subarrays/substrings that satisfy certain conditions
- Prefix sum: Efficient range sum queries
- HashMap for lookups: Reducing time complexity from O(n²) to O(n)
Example: Two Sum Problem
Given an array of integers and a target, return indices of two numbers that add up to the target.
Brute force would be O(n²). Use HashMap to store each number’s complement (target – num). When we find the complement in the map, we have our solution in O(n) time with O(n) space.
public int[] twoSum(int[] nums, int target) {
Map<Integer, Integer> map = new HashMap<>();
for (int i = 0; i < nums.length; i++) {
int complement = target – nums[i];
if (map.containsKey(complement)) {
return new int[] { map.get(complement), i };
}
map.put(nums[i], i);
}
return new int[] {};
}
Time: O(n) – Single pass through the array
Space: O(n) – HashMap stores up to n elements
Target = 9, Solution: nums[0] + nums[1] = 2 + 7 = 9
Two Pointer Technique
Efficient array traversal patterns
When to Use Two Pointers
The two-pointer technique is ideal for problems involving sorted arrays or linked lists where you need to find pairs meeting certain criteria. Common patterns include:
- Opposite ends: One pointer at start, one at end (e.g., Two Sum II)
- Fast & slow: Detect cycles in linked lists
- Window boundaries: Similar to sliding window but for specific conditions
Example: Container With Most Water
Given n non-negative integers representing an elevation map, find two lines that together with the x-axis form a container that holds the most water.
The area is limited by the shorter line. Start with widest container (pointers at ends), then move the pointer at the shorter line inward, as moving the longer line inward can’t increase the area.
def maxArea(height):
left, right = 0, len(height) – 1
max_area = 0
while left < right:
# Calculate current area
width = right – left
current_height = min(height[left], height[right])
max_area = max(max_area, width * current_height)
# Move pointer at shorter line
if height[left] < height[right]:
left += 1
else:
right -= 1
return max_area
Time: O(n) – Single pass through the array
Space: O(1) – Only two pointers and a few variables
Pointers move inward, always moving the shorter line to potentially find taller lines
Trees & Binary Search Trees
Hierarchical data structures and efficient searching
Tree Fundamentals
Trees are hierarchical data structures with a root node and child nodes. Binary trees have at most two children per node. Binary Search Trees (BST) maintain order: left child < parent < right child.
Common Tree Traversals
- Pre-order: Root → Left → Right (good for copying trees)
- In-order: Left → Root → Right (returns BST nodes in sorted order)
- Post-order: Left → Right → Root (good for deletion)
- Level-order: Breadth-first traversal using a queue
Example: Validate Binary Search Tree
Given the root of a binary tree, determine if it is a valid BST (all left descendants < node < all right descendants).
Don’t just check if left child < parent < right child. You must ensure ALL left subtree nodes are less than parent and ALL right subtree nodes are greater. Use range boundaries during traversal.
public boolean isValidBST(TreeNode root) {
return validate(root, null, null);
}
private boolean validate(TreeNode node, Integer low, Integer high) {
if (node == null) return true;
// Current node must be between low and high
if ((low != null && node.val <= low) || (high != null && node.val >= high)) {
return false;
}
// Recursively validate left and right subtrees
return validate(node.left, low, node.val) && validate(node.right, node.val, high);
}
Time: O(n) – Visit each node once
Space: O(h) – Call stack depth equals tree height (O(log n) for balanced, O(n) for skewed)
Valid BST: All left descendants < parent < all right descendants
Common DSA Problem Patterns
DSA Interview Success Tips
4-Week Blind-75 Mastery Plan
Daily: Solve 2-3 problems (1-2 hours). Weekly: Review all solved problems and identify patterns (2-3 hours). Weekends: Mock interviews and timed practice sessions (3-4 hours). Consistency matters more than marathon sessions.
