Array Questions at Rubrik: What to Expect

Array questions dominate Rubrik's technical interview, making up nearly half of their tagged problems. For a company specializing in data management, cloud data backup, and security, this focus is logical. Arrays are the fundamental data structure for handling data streams, file blocks, backup metadata, and time-series logs. Mastering array manipulation is not just an algorithmic exercise at Rubrik; it's a direct proxy for your ability to reason about and process the structured data that forms the core of their products. Strong performance here demonstrates the precise, efficient thinking required to build and scale their systems.

What to Expect — Types of Problems

Rubrik's array problems test a few key areas critical to their domain. You won't see many simple iterations; expect questions that require deeper pattern recognition.

• Sliding Window & Two Pointers: These are paramount. Given Rubrik's work with data streams and continuous backup, questions often involve analyzing contiguous subarrays or sequences. Look for problems dealing with "subarrays," "contiguous," "windows," or "substrings" that meet a specific condition (e.g., maximum sum, longest length with a constraint).
• In-Place Array Modification: Efficiently rearranging data within an array is a common theme, mirroring real-world data reorganization tasks. This includes problems like moving zeros, deduplication, or applying rotations without extra space.
• Prefix Sum & Hashing: Questions that involve calculating ranges, checking for subarray sums, or finding pairs/triplets often leverage prefix sums or hash maps (dictionaries) for optimal time complexity. This pattern is essential for quick aggregate queries on data.
• Simulation & Index Manipulation: Some problems require carefully navigating an array based on a set of rules, which tests your ability to translate a process into bug-free code—a key skill for implementing data policies or recovery logic.

How to Prepare — Study Tips with One Code Example

Focus on understanding the underlying pattern, not just memorizing solutions. For each problem type above, learn the standard algorithmic approach and its time/space complexity trade-offs. Practice verbalizing your thought process as you solve.

A fundamental pattern to master is the Two-Pointer technique for in-place modification. A classic example is moving all zeros in an array to the end while maintaining the relative order of 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. When the fast pointer finds a non-zero number, you swap it with the element at the slow pointer's position and increment the slow pointer. This achieves O(n) time and O(1) space.

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

Recommended Practice Order

Tackle problems in order of increasing conceptual dependency:

1. Basic In-Place Operations: Start with problems like Remove Duplicates from Sorted Array and Move Zeroes to build comfort with index manipulation.
2. Prefix Sum & Hashing: Practice problems like Two Sum and Subarray Sum Equals K to learn efficient lookup techniques.
3. Sliding Window Fundamentals: Solve fixed and dynamic window problems like Maximum Average Subarray I and Longest Substring Without Repeating Characters.
4. Complex Two-Pointer & Simulation: Finally, combine patterns with problems like Trapping Rain Water or Merge Intervals, which require managing multiple indices or complex conditions.

Practice Array at Rubrik