Having a dream is one thing, but to secure a software engineering role at a top tech company would require you to put in a little more effort. What lies between preparation and success is passing the technical interview with flying colors.
These interviews at tech companies are primarily designed to assess a candidate’s problem-solving skills, coding proficiency, and understanding of computer science fundamentals. While the questions may seem challenging at a glance, its clarity will come naturally to you with consistent practice, being able to identify the patterns that appear repetitively.
This guide presents a well-curated list of ten most classic coding interview questions, explaining the optimal approach for each, and why these problems matter in interviews. Your goal isn’t to memorize solutions, but to internalize the strategies to solve coding challenges even if structured in unfamiliar forms.
1. Two Sum
It tests the candidates’s ability to use hash maps efficiently and think beyond brute force.
Problem: Given an array of integers nums and an integer target, return the indices of the two numbers that add up to the target.
Optimal approach (Hash Map – O(n) time): Use a hash map to store each number’s index rather than checking all pairs (O(n²)). For each number x, calculate target-x. If the complement exists in the map, you got your pair. This solution trades O(n) extra space for runtime improvement.
Pro tip: Interviewers often follow up questions by asking how you’d handle duplicates or return multiple pairs.
2. Reverse a Linked List
A fundamental test to check knowledge associated to pointer manipulation and linked list.
Problem: Reverse a singly linked list and return the new head.
Optimal approach (Iterative – O(n) time): Maintain three pointers: prev (initially null), curr (head), and next_temp. Repeat the list, reversing each next pointer to point to prev. At the end, prev will be the new head.
Pro tip: Explain the recursive version, too, as it uses O(n) space on the call stack.
3. Valid parentheses
It tests the candidate’s understanding of stacks and parsing logic.
Problem: Given a string containing (, ), {, }, [, and ], determine if it’s valid.
Optimal approach (Stack – O(n) time): Push open brackets to a stack to navigate the string. For each closing bracket, pop and check for a match. If it’s mismatched or the stack is empty, return false. Outcome? The stack must be empty.
4. Contains duplicate
It’s a quick test of a candidate’s efficiency with common data structures and hash sets.
Problem: Given an integer array nums, determine if any value appears at least twice.
Optimal solution (Hash Set – O(n) time): Repeat and insert into a set. If an element already exists, return true. If the loop ends, return false. Space is O(n).
5. Maximum Subarray (Kadane’s Algorithm)
To check classic dynamic programming and greedy thinking.
Problem: Find the contiguous subarray with the largest sum.
Optimal approach (Kadane’s Algorithm – O(n) time): Track two variables: current_sum (max ending here) and max_sum (global max). For each element,
current_sum = max(nums[i], current_sum + nums[i])
and update max_sum. Any negative sum before index i can only hurt the result.
6. Product of Array except self
It tests array manipulation, prefix/suffix concepts, and avoiding division.
Problem: Return an array output such that output[i] equals the product of all numbers except nums[i].
Optimal approach (Prefix & Suffix – O(n) time):
- First pass: Calculate prefix products for each index.
- Second pass (right to left): Multiply each output by the suffix product.
This prevents division and handles zeroes correctly.
7. Merge Intervals
Common in scheduling problems, test sorting, and iteration.
Problem: Merge overlapping intervals.
Optimal approach (Sort + Merge – O(n log n) time): Classify intervals by start time. Iterate, merging with the last added interval if overlaps exist. Otherwise, append as new.
8. Binary Tree Level Order Traversal
It assesses knowledge of BFS and queues.
Problem: Return a tree’s level order traversal (nodes grouped by depth).
Optimal approach (Queue BFS – O(n) time): Use a queue, enqueuing the root first. For each level, dequeue all nodes, record values, enqueue their children, and continue.
9. Clone Graph
It tests the candidate’s ability to handle cycles, recursion, and hash maps.
Problem: Deep copy a graph.
Optimal solution (DFS or BFS + Hash Map – O(n) time): Use a map from the original → cloned node. Navigate via DFS or BFS, and clone neighbors recursively (or iteratively) while referencing the map to avoid duplication.
10. Search in Rotated Sorted Array
These questions combine binary search with careful logic.
Problem: Find a target in a rotated sorted array.
Optimal solution (Modified Binary Search – O(log n) time): Check whether the left half is sorted at every step. If the target lies there, search left; otherwise, search right. Continue the process until found or the search space is empty.
How to approach these problems in an interview
When storming your brain to solve these diverse problems in a timed interview environment;
- Talk through your reasoning before coding (interviewers value thought process as much as syntax).
- Be explicit about complexities (e.g., “This runs in O(n) time, O(1) space”)
- Always consider edge cases like empty arrays, large inputs, or single elements.
- It’s fine to start with a brute-force solution, just do not ignore optimizing it later.
These coding interview questions test the candidate’s mastery of core techniques such as hash maps, two pointers, sliding windows, dynamic programming, recursion, and graph traversal.
Now, reading theories and solutions is only the first stage. In addition to solo practice in your preferred language, it is also wise to leverage structured platforms like AlgoCademy to boost your learning and prep efficiency with curated interview-style problems, step-by-step explanations, reusable patterns, and a coding environment to solve coding challenges hands-on.
Final thoughts
To ensure success in technical interviews, focus on understanding the key patterns first, while keeping these ten problems as staples. Create a schedule and practice consistently with these problems and their variations to cement confidence and thinking like an engineer.
So, practice beyond your limit until these patterns become your second nature.
