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Databricks Coding Interview Questions

27 Databricks coding interview problems with full optimal solutions — 18 easy, 7 medium, 2 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Databricks interviewer values, and a FAQ section.

  • #16easyfoundational

    16. Valid Anagram

    Determine whether two strings are anagrams — Databricks surfaces this in early screens to test whether you reach for a frequency map, the same mental model behind deduplication passes in Delta Lake compaction jobs.

  • #17easyfoundational

    17. First Bad Version

    Find the first broken build in a sequence — a canonical binary-search probe that mirrors how Databricks bisects failing notebook versions or regressed MLflow runs in a CI pipeline.

  • #18easyfoundational

    18. Counting Bits

    Count set bits for every integer 0–n — a DP warm-up that directly parallels how Databricks computes per-partition popcount statistics in Photon's vectorized execution engine.

  • #19mediumfoundational

    19. Top K Frequent Elements

    Return the k most frequent integers — the canonical heap-vs-bucket-sort duel that Databricks maps directly to top-N analytics queries and the cardinality-estimation problems inside Delta Live Tables.

  • #20mediumfoundational

    20. Min Stack

    Design a stack that retrieves its minimum in O(1) — Databricks uses this to test auxiliary-state discipline, a pattern that shows up when tracking minimum-cost DAG nodes in a query optimizer.

  • #21mediumfoundational

    21. Find Peak Element

    Locate any local maximum in O(log n) — Databricks ties this to binary-search strategies for finding optimal partition-split points in Delta Lake's data-skipping index.

  • #22mediumfoundational

    22. Course Schedule

    Detect a cycle in a directed prerequisite graph — the textbook DAG-validation problem that Databricks applies directly to detecting circular dependencies in Delta Live Tables pipeline DAGs.

  • #23mediumfoundational

    23. Partition Labels

    Greedily partition a string so each character appears in exactly one part — a range-merging pattern Databricks reuses when computing non-overlapping file-range compaction windows in Delta Lake's OPTIMIZE command.

  • #24mediumfoundational

    24. Subarray Sum Equals K

    Count contiguous subarrays whose values sum to k — the prefix-sum technique here is the same one Databricks uses to compute rolling aggregations over unbounded streaming windows in Structured Streaming.

  • #25mediumfoundational

    25. Number of Islands

    Count connected land components in a 2-D grid — a BFS/DFS connected-components pattern Databricks extends to counting disconnected data-lake zones and partitioning graph-based cluster topology.

  • #26hardfoundational

    26. Sliding Window Maximum

    Return the maximum in every sliding window of size k — a deque-based streaming aggregation Databricks implements in Structured Streaming's watermark-bounded window queries over high-throughput event streams.

  • #27hardfoundational

    27. Serialize and Deserialize Binary Tree

    Encode and reconstruct an arbitrary binary tree through a string — a serialization-format problem Databricks faces when checkpointing execution-plan trees in Delta's query optimizer and persisting MLflow model dependency graphs.

  • #1easyfrequently asked

    1. Two Sum

    Given an array of integers, return indices of the two numbers that add up to a target. Databricks uses this as a warm-up to see if you naturally reach for a hash map and to gauge whether you can articulate the brute-force-to-optimal tradeoff in distributed terms.

  • #2easyfrequently asked

    2. Valid Parentheses

    Determine if a string of brackets is balanced. Databricks asks this to see if you reach for a stack instinctively and whether you can map it onto SQL-parser or query-AST validation scenarios.

  • #3easyfrequently asked

    3. Merge Two Sorted Lists

    Merge two sorted linked lists into one sorted list. Databricks uses this as a launchpad to the real question they care about: how does this generalize to merging K sorted partitions during a shuffle?

  • #4easysometimes asked

    4. Remove Duplicates from Sorted Array

    Modify a sorted array in-place to remove duplicates and return the new length. Databricks uses this to test the two-pointer / read-write head pattern that shows up in every distributed dedup operator.

  • #5easysometimes asked

    5. Remove Element

    Remove all occurrences of a value from an array in-place. Databricks uses this as the in-place-filter primitive that maps onto Spark's filter operator on a partition.

  • #6easysometimes asked

    6. Search Insert Position

    Given a sorted array, return the index where a target should be inserted to keep it sorted. Databricks uses this to verify you can write a binary search that returns the LEFT bound, which is the canonical primitive for range partitioning.

  • #7easyfrequently asked

    7. Maximum Subarray

    Find the contiguous subarray with the largest sum. Databricks asks this to test Kadane's algorithm and to set up the harder question: 'now do it on a Spark DataFrame partitioned across the cluster.'

  • #8easyrarely asked

    8. Plus One

    Given a non-empty array of digits representing a non-negative integer, add one to the integer. Databricks asks this to see if you handle the carry-propagation cleanly and whether you reach for in-place mutation when the structure allows.

  • #9easysometimes asked

    9. Merge Sorted Array

    Merge two sorted arrays into the first one, in-place, where the first has trailing space to hold the result. Databricks uses this to test the back-to-front merge trick, which is the same memory-efficient pattern their sort-merge join uses.

  • #10easysometimes asked

    10. Binary Tree Inorder Traversal

    Return the inorder traversal of a binary tree's nodes' values. Databricks asks this to see if you can write both the recursive and iterative versions and explain why the iterative one matters in JVM-stack-bounded environments.

  • #11easyrarely asked

    11. Same Tree

    Check whether two binary trees are structurally identical with the same values. Databricks uses this to test recursive pattern-matching, which is the same template Catalyst uses to compare query subtrees during optimizer rule application.

  • #12easyrarely asked

    12. Symmetric Tree

    Determine if a binary tree is a mirror of itself around its center. Databricks asks this to test paired recursion — comparing two pointers that walk in opposite directions, which is the same primitive used in plan-folding and palindrome detection.

  • #13easyfrequently asked

    13. Maximum Depth of Binary Tree

    Find the maximum depth of a binary tree. Databricks uses this to test the canonical 'return aggregated value upward' tree recursion that maps directly onto cost estimation in Catalyst.

  • #14easysometimes asked

    14. Balanced Binary Tree

    Determine if a binary tree is height-balanced. Databricks asks this to test the post-order pattern where you return information up the tree to avoid recomputing heights at every node.

  • #15easysometimes asked

    15. Minimum Depth of Binary Tree

    Find the minimum depth of a binary tree (distance from root to nearest LEAF). Databricks asks this because it tests whether you can distinguish 'null child' from 'leaf' — a subtle case that catches candidates who only memorized max-depth.

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Databricks Coding Interview Questions — Full Solutions — InterviewChamp.AI