Array Questions at Databricks: What to Expect

Array questions dominate Databricks interviews, making up 18 of their 31 most frequently asked problems. This isn't a coincidence. Databricks builds its platform on large-scale data processing (Spark), where operations on distributed datasets often map conceptually to core array manipulations—transforming, filtering, aggregating, and joining sequences of data. Mastering arrays demonstrates your ability to think about data in memory, handle indices and state efficiently, and implement the foundational logic that scales to big data workloads. Strong performance here is non-negotiable.

What to Expect — Types of Problems

The array problems at Databricks test a focused set of patterns. Expect heavy emphasis on in-place array manipulation, where you must modify the input array using O(1) extra space. This tests your grasp of pointers and careful element swapping. Sliding Window problems are also prevalent, essential for analyzing contiguous subsequences, which is a common pattern in time-series or log analysis. You will frequently encounter two-pointer techniques, used for tasks like partitioning, searching in sorted arrays, or detecting cycles. Finally, be prepared for problems that blend arrays with hashing (for fast lookups) and prefix sums (for efficient range queries). The difficulty often lies not in complex data structures, but in executing these core patterns flawlessly under constraints.

How to Prepare — Study Tips with One Code Example

Focus on pattern recognition, not memorization. For each problem, identify the core technique before writing code. Practice drawing the array and stepping through your logic with edge cases (empty, single element, duplicates). Time yourself to build speed.

A quintessential pattern is the Two-Pointer technique for in-place removal. Here’s how to implement the "Remove Duplicates from Sorted Array" problem, which is a classic test of in-place manipulation.

def removeDuplicates(nums):
    if not nums:
        return 0
    write_index = 1
    for read_index in range(1, len(nums)):
        if nums[read_index] != nums[write_index - 1]:
            nums[write_index] = nums[read_index]
            write_index += 1
    return write_index

The key is maintaining write_index as the boundary for the unique portion. This pattern generalizes to many in-place update problems.

Recommended Practice Order

Build competence sequentially. Start with fundamental in-place operations (remove element, move zeroes). Next, master two-pointer patterns for searching and partitioning. Then, tackle sliding window problems for subarrays. Finally, integrate hashing and prefix sums for more complex challenges. Always prioritize writing clean, correct code for the core patterns over solving esoteric problems.

Practice Array at Databricks