Top 10 Technical Interview Questions (with AI Analysis)

Technical interviews can be daunting, but knowing what to expect makes all the difference. Based on analysis of thousands of real interviews, we've identified the top 10 technical questions that appear most frequently across top tech companies.

Why These Questions Matter

These questions test fundamental computer science concepts and problem-solving abilities. Mastering them demonstrates:

• Strong foundation in algorithms and data structures
• Problem-solving skills under pressure
• Code quality and best practices
• Communication ability while coding

The Top 10 Questions

1. Two Sum Problem

Difficulty: Easy
Frequency: Very High
Companies: Amazon, Google, Microsoft, Meta

Problem Statement

Given an array of integers and a target sum, find two numbers that add up to the target.

def two_sum(nums, target):
    """
    Example:
    Input: nums = [2, 7, 11, 15], target = 9
    Output: [0, 1]
    Explanation: nums[0] + nums[1] = 2 + 7 = 9
    """
    hash_map = {}
    
    for i, num in enumerate(nums):
        complement = target - num
        if complement in hash_map:
            return [hash_map[complement], i]
        hash_map[num] = i
    
    return []

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(n)
• Key Insight: Use hash map for O(1) lookups
• Common Mistakes: Trying to use nested loops (O(n²))

What Interviewers Look For

• Understanding of hash tables
• Ability to optimize from brute force
• Edge case handling (empty array, no solution)

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2. Reverse a Linked List

Difficulty: Medium
Frequency: Very High
Companies: Apple, Amazon, LinkedIn

Problem Statement

Reverse a singly linked list.

function reverseList(head) {
    let prev = null;
    let current = head;
    
    while (current !== null) {
        let next = current.next;
        current.next = prev;
        prev = current;
        current = next;
    }
    
    return prev;
}

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(1)
• Key Insight: Three-pointer technique
• Alternative: Recursive solution (uses O(n) stack space)

What Interviewers Look For

• Pointer manipulation skills
• Understanding of linked list structure
• Ability to explain iterative vs recursive approaches

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3. Valid Parentheses

Difficulty: Easy
Frequency: High
Companies: Google, Meta, Airbnb

Problem Statement

Determine if a string containing brackets is valid (properly closed and nested).

def is_valid(s):
    """
    Example:
    Input: "({[]})"
    Output: True
    
    Input: "([)]"
    Output: False
    """
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}
    
    for char in s:
        if char in mapping:
            top = stack.pop() if stack else '#'
            if mapping[char] != top:
                return False
        else:
            stack.append(char)
    
    return not stack

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(n)
• Key Insight: Stack for matching pairs
• Edge Cases: Empty string, unbalanced brackets

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4. Binary Tree Maximum Depth

Difficulty: Easy
Frequency: High
Companies: Amazon, Microsoft, Apple

Problem Statement

Find the maximum depth of a binary tree.

function maxDepth(root) {
    if (root === null) return 0;
    
    const leftDepth = maxDepth(root.left);
    const rightDepth = maxDepth(root.right);
    
    return Math.max(leftDepth, rightDepth) + 1;
}

// Iterative BFS solution
function maxDepthIterative(root) {
    if (!root) return 0;
    
    let queue = [root];
    let depth = 0;
    
    while (queue.length > 0) {
        let levelSize = queue.length;
        depth++;
        
        for (let i = 0; i < levelSize; i++) {
            let node = queue.shift();
            if (node.left) queue.push(node.left);
            if (node.right) queue.push(node.right);
        }
    }
    
    return depth;
}

AI Analysis

• Recursive: O(n) time, O(h) space (h = height)
• Iterative: O(n) time, O(w) space (w = width)
• Key Insight: DFS vs BFS trade-offs

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5. Merge Two Sorted Lists

Difficulty: Easy
Frequency: High
Companies: LinkedIn, Amazon, Google

Problem Statement

Merge two sorted linked lists into one sorted list.

def merge_two_lists(l1, l2):
    dummy = ListNode(0)
    current = dummy
    
    while l1 and l2:
        if l1.val < l2.val:
            current.next = l1
            l1 = l1.next
        else:
            current.next = l2
            l2 = l2.next
        current = current.next
    
    # Attach remaining nodes
    current.next = l1 if l1 else l2
    
    return dummy.next

AI Analysis

• Time Complexity: O(n + m)
• Space Complexity: O(1)
• Pattern: Two-pointer merge technique
• Application: Foundation for merge sort

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6. Best Time to Buy and Sell Stock

Difficulty: Easy
Frequency: Very High
Companies: Meta, Amazon, Apple, Google

Problem Statement

Find the maximum profit from buying and selling a stock once.

function maxProfit(prices) {
    let minPrice = Infinity;
    let maxProfit = 0;
    
    for (let price of prices) {
        minPrice = Math.min(minPrice, price);
        maxProfit = Math.max(maxProfit, price - minPrice);
    }
    
    return maxProfit;
}

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(1)
• Key Insight: Track minimum price seen so far
• Optimization: Single pass solution

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7. Climbing Stairs

Difficulty: Easy
Frequency: High
Companies: Google, Amazon, Adobe

Problem Statement

Count ways to climb n stairs (1 or 2 steps at a time).

def climb_stairs(n):
    """
    Dynamic Programming approach
    It's essentially Fibonacci sequence!
    """
    if n <= 2:
        return n
    
    # dp[i] = ways to reach step i
    prev2 = 1  # ways to reach step 1
    prev1 = 2  # ways to reach step 2
    
    for i in range(3, n + 1):
        current = prev1 + prev2
        prev2 = prev1
        prev1 = current
    
    return prev1

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(1) optimized
• Pattern: Classic DP problem
• Connection: Fibonacci sequence

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8. Longest Substring Without Repeating Characters

Difficulty: Medium
Frequency: Very High
Companies: Amazon, Meta, Microsoft

Problem Statement

Find the length of the longest substring without repeating characters.

function lengthOfLongestSubstring(s) {
    let charSet = new Set();
    let left = 0;
    let maxLength = 0;
    
    for (let right = 0; right < s.length; right++) {
        while (charSet.has(s[right])) {
            charSet.delete(s[left]);
            left++;
        }
        charSet.add(s[right]);
        maxLength = Math.max(maxLength, right - left + 1);
    }
    
    return maxLength;
}

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(min(n, m)) where m = charset size
• Pattern: Sliding window technique
• Key Skill: Two-pointer approach

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9. Product of Array Except Self

Difficulty: Medium
Frequency: High
Companies: Meta, Amazon, Microsoft

Problem Statement

Return array where each element is the product of all other elements.

def product_except_self(nums):
    """
    Cannot use division, must be O(n)
    """
    n = len(nums)
    result = [1] * n
    
    # Left pass: product of all elements to the left
    left_product = 1
    for i in range(n):
        result[i] = left_product
        left_product *= nums[i]
    
    # Right pass: multiply by product of all elements to the right
    right_product = 1
    for i in range(n - 1, -1, -1):
        result[i] *= right_product
        right_product *= nums[i]
    
    return result

AI Analysis

• Time Complexity: O(n)
• Space Complexity: O(1) excluding output
• Trick: Two-pass solution
• Constraint: No division allowed

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10. LRU Cache

Difficulty: Medium
Frequency: High
Companies: Amazon, Google, Meta, Apple

Problem Statement

Implement Least Recently Used cache with O(1) operations.

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}  # key -> node
        self.head = Node(0, 0)  # dummy head
        self.tail = Node(0, 0)  # dummy tail
        self.head.next = self.tail
        self.tail.prev = self.head
    
    def get(self, key):
        if key in self.cache:
            node = self.cache[key]
            self._remove(node)
            self._add(node)
            return node.value
        return -1
    
    def put(self, key, value):
        if key in self.cache:
            self._remove(self.cache[key])
        
        node = Node(key, value)
        self._add(node)
        self.cache[key] = node
        
        if len(self.cache) > self.capacity:
            lru = self.head.next
            self._remove(lru)
            del self.cache[lru.key]
    
    def _remove(self, node):
        node.prev.next = node.next
        node.next.prev = node.prev
    
    def _add(self, node):
        # Add to tail (most recently used)
        node.prev = self.tail.prev
        node.next = self.tail
        self.tail.prev.next = node
        self.tail.prev = node

AI Analysis

• Time Complexity: O(1) for both operations
• Data Structures: HashMap + Doubly Linked List
• Key Insight: Combine structures for optimal performance
• Design Pattern: Common system design question

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Preparation Strategy

Week 1-2: Foundation

• Master data structures (arrays, linked lists, trees)
• Practice basic problems daily
• Understand time/space complexity

Week 3-4: Pattern Recognition

• Learn common patterns (sliding window, two pointers, DFS/BFS)
• Practice medium difficulty problems
• Focus on explaining your thought process

Week 5-6: Advanced Practice

• Mock interviews with AI feedback
• System design basics
• Review and refine weak areas

How AI Can Help Your Preparation

1. Instant Feedback

Get real-time analysis of your solution's efficiency and correctness.

2. Pattern Recognition

AI can identify which algorithmic pattern your problem follows.

3. Multiple Solutions

See different approaches (brute force, optimized, elegant) for the same problem.

4. Interview Simulation

Practice explaining your code to an AI interviewer just like in real interviews.

Common Mistakes to Avoid

1. Jumping to Code Too Quickly

Always clarify requirements and discuss approach first.

2. Ignoring Edge Cases

# Don't forget:
- Empty input
- Single element
- Duplicate values
- Negative numbers
- Integer overflow

3. Poor Communication

Explain your thought process while coding. Silence is concerning to interviewers.

4. Not Testing Your Code

Always walk through test cases, including edge cases.

5. Giving Up Too Soon

Ask clarifying questions. Interviewers want to see your problem-solving process.

Practice Resources

• LeetCode: Most popular platform with company-specific questions
• HackerRank: Good for beginners, clear explanations
• ManyOffer AI: Get instant feedback on your coding solutions

Conclusion

These 10 questions form the foundation of technical interview preparation. Master them and you'll be ready for 80% of coding interviews.

Remember:

• Understand the problem before coding
• Communicate your approach clearly
• Write clean, testable code
• Analyze time and space complexity
• Practice with AI for instant feedback

Ready to practice these questions with AI-powered feedback? Start your practice session and get personalized insights on your coding solutions.

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Quick Reference

| Question | Difficulty | Pattern | Time | Space | |----------|-----------|---------|------|-------| | Two Sum | Easy | Hash Map | O(n) | O(n) | | Reverse List | Medium | Pointers | O(n) | O(1) | | Valid Parentheses | Easy | Stack | O(n) | O(n) | | Tree Depth | Easy | DFS/BFS | O(n) | O(h) | | Merge Lists | Easy | Two Pointers | O(n) | O(1) | | Stock Profit | Easy | Greedy | O(n) | O(1) | | Climb Stairs | Easy | DP | O(n) | O(1) | | Longest Substring | Medium | Sliding Window | O(n) | O(m) | | Product Array | Medium | Two Pass | O(n) | O(1) | | LRU Cache | Medium | HashMap + DLL | O(1) | O(n) |

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Practice these questions with ManyOffer's AI interview platform and receive instant, personalized feedback on your solutions.