What are the most common array patterns in coding interviews?

The four most common array patterns are: two pointers (for sorted arrays and meeting-in-the-middle problems), sliding window (for subarray/substring problems), prefix sum (for range query problems), and sorting-based techniques (for finding pairs/triplets). Mastering these four covers roughly 80% of array interview questions.

What is the two-pointer technique?

The two-pointer technique uses two indices that move through an array, typically one from each end or both moving in the same direction. It solves problems like finding a pair that sums to a target, removing duplicates from a sorted array, and container with most water. Time complexity is O(n) vs O(n^2) for nested loops.

How common are array questions in FAANG interviews?

Arrays and strings account for roughly 35% of all coding interview questions at FAANG. They appear in every company and at every level. Junior candidates see more pure array manipulation. Senior candidates see arrays combined with other data structures (heaps, hash maps) or as part of larger system-level problems.

What is the sliding window technique?

Sliding window maintains a contiguous subarray that expands and contracts as you move through the array. It solves problems like longest substring without repeating characters, maximum sum subarray of size k, and minimum window substring. Time complexity is O(n) vs O(n^2) or O(n^3) for naive approaches.

Top Array Interview Questions 2026

Arrays account for roughly 35% of all coding interview questions. The patterns are limited and learnable: two pointers, sliding window, prefix sum, and sorting-based techniques.

Quick Answer

The four array patterns that cover most interview questions are: two pointers, sliding window, prefix sum, and sorting-based approaches. Arrays account for roughly 35% of all FAANG coding questions. Most array problems can be reduced to one of these patterns once you recognize the structure of the problem.

Two-Pointer Technique

Two pointers is the most common array technique in interviews. Classic problems: remove duplicates from sorted array (O(n) vs O(n) with a set), check if an array contains a pair summing to a target (sorted: O(n)), container with most water.

Template: one pointer at each end, converge toward the center. Decision logic determines which pointer moves. Time O(n), space O(1). Applies when the array is sorted or when you need to find pairs/triplets satisfying a condition.

Sliding Window

Sliding window maintains a range [left, right] and expands/shrinks it based on a constraint. Fixed-size window: move both pointers together. Variable-size window: expand right until constraint violated, shrink left until satisfied.

Classic problems: maximum sum subarray of size k (fixed window), longest substring without repeating characters (variable window with set), minimum window substring (variable with frequency map). Master these three and you cover 80% of sliding window variants.

Prefix Sums

A prefix sum array allows O(1) range sum queries after O(n) preprocessing. prefix[i] = prefix[i-1] + arr[i]. Sum of arr[l..r] = prefix[r] - prefix[l-1]. Extend to 2D for submatrix sums.

Prefix sums combined with a hash map solve subarray sum = k in O(n): track how many times each prefix sum has occurred. If prefix[i] - k appears in the map, a subarray ending at i has sum k.

In-Place and Rotation

Rotate array by k positions: three-reversal algorithm O(n) time O(1) space. Reverse entire array, reverse first k, reverse remaining n-k. Cyclic replacement is an alternative O(n) time O(1) space approach.

In-place rearrangement: move all zeros to end while maintaining order (two pointer, one pass). Separate positive and negative numbers (not stable). Merge two sorted halves without extra space (more complex).

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