Array Questions at Oracle: What to Expect

Array questions make up nearly half of Oracle’s technical interview problem pool (160 out of 340 total). This isn’t by accident. Arrays are the fundamental data structure for representing ordered data, and they underpin countless real-world systems—from database record sets and query results to in-memory caches and configuration lists. At Oracle, a company built on data management, your ability to efficiently manipulate, search, and transform array data is a direct test of your core engineering competency. Expect these questions to assess not just your coding skill, but your understanding of memory, time complexity, and edge-case handling in a performance-critical context.

What to Expect — Types of Problems

Oracle’s array problems tend to focus on practical application over abstract puzzle-solving. You can broadly categorize them into three types:

1. In-Place Manipulation: Questions that require modifying the array without using significant extra space. This tests your grasp of two-pointer techniques, swapping, and partitioning. Examples include rotating an array, moving zeroes to the end, or segregating even and odd numbers.
2. Subarray & Range Analysis: Problems involving contiguous segments of an array. This includes finding subarrays with a given sum, maximum product subarray, or range update queries. These often test your ability to optimize from a brute-force O(n²) approach to an O(n) solution using hashing or sliding window techniques.
3. Multi-Array Processing: Challenges involving two or more arrays, such as merging sorted arrays, finding the intersection or union, or computing medians of two sorted arrays. These assess your logic for synchronously iterating through multiple data sequences.

How to Prepare — Study Tips with One Code Example

Focus on mastering patterns, not memorizing problems. For each question, identify the core technique. Is it a sliding window? Two-pointers? A prefix sum? Practice deriving the algorithm from first principles. Always analyze time and space complexity aloud during your thought process.

A fundamental pattern is the Two-Pointer Technique for in-place manipulation. Consider the classic problem: "Given an integer array nums, move all 0s to the end while maintaining the relative order of the non-zero elements."

The optimal approach uses a "slow" pointer to mark the position for the next non-zero element and a "fast" pointer to scan through the array.

def moveZeroes(nums):
    slow = 0
    for fast in range(len(nums)):
        if nums[fast] != 0:
            nums[slow], nums[fast] = nums[fast], nums[slow]
            slow += 1
    # Modification is in-place

This approach runs in O(n) time and O(1) space, which is the expected optimal solution.

Recommended Practice Order

Build your skills progressively. Start with basic traversal and counting problems. Then, master the two-pointer technique for in-place operations. Move on to sliding window problems for subarrays, followed by prefix sum and hashing techniques for more complex sum queries. Finally, tackle the challenging multi-array problems. This layered approach ensures you have the foundational patterns solid before combining them.

Practice Array at Oracle