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.
Showing 17 problems of 27
- #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?
- #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.'
- #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.
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