💻 Technical Interview Questions: Complete Guide with 150+ Problems

💻 Technical Interview Questions: Complete Guide

Technical interviews are the make-or-break component of landing a job at top tech companies. They test your ability to solve problems, code under pressure, and think through complex systems. With proper preparation, you can master them.

This guide covers 150+ real technical interview questions, data structures, algorithms, system design, and proven strategies used by engineers at Google, Amazon, Microsoft, and other FAANG companies.

🎯 Types of Technical Interview Questions

1. Coding/Algorithm Problems (50-60% of interview)

These test your ability to solve problems efficiently using code. You’re given a problem and need to write working code in 30-45 minutes.

2. System Design Questions (20-30% of interview)

These test your ability to design large-scale systems. You’re asked to design something like YouTube, Twitter, Uber, etc.

3. Technical Knowledge Questions (10-20% of interview)

These test your knowledge of specific technologies: databases, caching, APIs, cloud services, etc.

4. Behavioral + Technical Questions (10-20% of interview)

These combine behavioral questions with technical context: “Tell me about a time you optimized code performance”

📚 Data Structures You Must Know

Essential Data Structures:

🔧 Key Algorithms You Must Know

Sorting Algorithms:

• Merge Sort: O(n log n) – Stable, divide-and-conquer
• Quick Sort: O(n log n) average – In-place, fast
• Heap Sort: O(n log n) – In-place, guaranteed
• Bubble/Selection/Insertion: O(n²) – For small datasets

Searching Algorithms:

• Binary Search: O(log n) – Must know by heart
• BFS (Breadth-First Search): O(V+E) – Level order
• DFS (Depth-First Search): O(V+E) – Recursive/stack

Advanced Algorithms:

• Dynamic Programming: O(n) or O(n²) – Memoization, bottom-up
• Greedy: Local optimal – Activity selection, huffman coding
• Dijkstra: O((V+E) log V) – Shortest path
• Topological Sort: O(V+E) – Dependency graphs

💯 100+ Coding Interview Problems by Difficulty

EASY Problems (30 Questions)

MEDIUM Problems (60 Questions)

HARD Problems (20 Questions)

🏗️ System Design Questions

Classic System Design Problems:

System Design Approach (STAR Framework):

🔍 Example Coding Solution

Problem: Two Sum (Easy)

Question: Given an array of integers nums and an integer target, return the indices of the two numbers that add up to target. Each input has exactly one solution. You can’t use the same element twice.

def twoSum(nums, target):
    """
    Using HashMap approach:
    - Iterate through array once
    - For each number, check if (target - number) exists
    - Store numbers in hash map for O(1) lookup
    """
    num_map = {}  # Value -> Index mapping
    
    for i, num in enumerate(nums):
        complement = target - num
        
        if complement in num_map:
            # Found the pair!
            return [num_map[complement], i]
        
        # Store current number for future lookups
        num_map[num] = i
    
    return []  # No solution found

# Test Cases
assert twoSum([2, 7, 11, 15], 9) == [0, 1]
assert twoSum([3, 2, 4], 6) == [1, 2]
assert twoSum([3, 3], 6) == [0, 1]

# Time Complexity: O(n) - single pass
# Space Complexity: O(n) - hash map storage
            

Interview Tips for This Problem:

• ✅ Clarify: “Can I assume there’s always a solution?” “Can I use same element twice?”
• ✅ Approach: “Brute force would be O(n²) with nested loop. Can optimize with HashMap to O(n).”
• ✅ Code: Write clean, readable code with comments
• ✅ Test: Test with normal, edge, and corner cases
• ✅ Optimize: Discuss time/space tradeoffs

📋 Preparation Strategy

Month 1: Foundation (Weeks 1-4)

• Week 1: Learn data structures deeply
• Week 2: Learn sorting and searching algorithms
• Week 3: Master two pointers, sliding window
• Week 4: Do 20 easy problems on LeetCode

Month 2: Core Skills (Weeks 5-8)

• Week 5: Trees and binary search trees
• Week 6: Graphs (BFS, DFS)
• Week 7: Dynamic Programming basics
• Week 8: Do 40 medium problems

Month 3: Advanced & Practice (Weeks 9-12)

• Week 9: Advanced dynamic programming
• Week 10: System design basics
• Week 11: Mock interviews
• Week 12: Do 20+ hard problems, final review

Daily Schedule (1-2 hours):

• ⏱️ 20 min: Review data structures/algorithms
• ⏱️ 30-40 min: Solve coding problem on LeetCode
• ⏱️ 10-15 min: Review solution and discuss approach
• ⏱️ Weekends: Mock interviews, hard problems

✅ Pro Tips for Technical Interviews

During the Interview:

• ✅ Think out loud: Explain your approach as you code
• ✅ Ask clarifying questions: Confirm assumptions
• ✅ Start with brute force: Then optimize
• ✅ Code cleanly: Use meaningful variable names
• ✅ Test your code: With normal and edge cases
• ✅ Discuss complexity: Time and space tradeoffs
• ✅ Be confident: Even if you don’t know something
• ✅ Ask for hints: If truly stuck (shows humility)

What Interviewers Look For:

• ✅ Can you understand the problem?
• ✅ Can you think of multiple approaches?
• ✅ Can you implement code correctly?
• ✅ Can you optimize and think about tradeoffs?
• ✅ Can you test and debug?
• ✅ Can you communicate clearly?
• ✅ Are you confident in your knowledge?

📚 Resources for Practice