Landing a role at Databricks, a leader in data and AI, requires more than just a strong resume; it demands a deep understanding of core computer science principles and the ability to solve complex technical challenges under pressure. Databricks interviews are renowned for their rigor, often featuring highly technical rounds that dive into algorithms, data structures, and concurrency. Candidates typically face problems at LeetCode medium to hard levels, with a significant emphasis on optimizing solutions and handling edge cases. This comprehensive guide outlines the most frequently encountered Databricks interview questions, offering insights into why they are asked, how to approach them, and concise example answers. Preparing these common problems will give you a solid foundation for demonstrating your problem-solving prowess and securing your desired position at Databricks.
What Are Databricks Interview Questions?
Databricks interview questions are primarily focused on assessing a candidate's technical depth, particularly in areas crucial for building and maintaining large-scale data processing platforms. These questions typically fall into several key categories: data structures and algorithms, system design, and concurrency/multithreading. Expect problems that test your proficiency with arrays, strings, trees, graphs, heaps, and dynamic programming. Many questions are drawn from popular coding platforms like LeetCode, often requiring optimized solutions beyond brute force. Given Databricks' emphasis on distributed computing and big data, interviewers also probe into how you handle concurrent operations and design scalable systems. The overall goal is to evaluate your analytical thinking, coding clarity, and ability to engineer efficient and robust solutions applicable to Databricks' core technologies.
Why Do Interviewers Ask Databricks Interview Questions?
Interviewers at Databricks ask these specific types of questions to thoroughly evaluate a candidate's foundational computer science knowledge and practical problem-solving abilities. For instance, algorithmic questions assess your analytical thinking and efficiency, crucial for developing high-performance data processing solutions. Data structure questions verify your understanding of how to store and manage data effectively, directly impacting the performance of Databricks' products. Concurrency and multithreading questions are vital for roles dealing with distributed systems, ensuring you can write thread-safe and scalable code. System design questions evaluate your ability to architect robust, scalable, and fault-tolerant systems, mirroring the challenges faced in Databricks' platform development. Ultimately, these questions help Databricks identify candidates who can contribute to their mission of unifying data, analytics, and AI.
Design Hit Counter
Design Tic-Tac-Toe
String to Integer (atoi)
House Robber
House Robber II
IP to CIDR
Median of Two Sorted Arrays
Longest Substring Without Repeating Characters
Merge Intervals
Word Ladder
Find K-th Largest Element in an Array
Serialize and Deserialize Binary Tree
Count of Smaller Numbers After Self
LRU Cache
Validate Binary Search Tree
Course Schedule
Number of Islands
Product of Array Except Self
Maximum Subarray
Kth Smallest Element in a BST
Word Search
Find Duplicate Subtrees
Top K Frequent Elements
Sliding Window Maximum
Implement Trie (Prefix Tree)
Maximum Sum Rectangle in 2D Matrix
Median Finder
Concurrency: Logger Rate Limiter
Thread-Safe Bounded Blocking Queue
Implement Concurrent Counter
Preview List
1. How would you design a hit counter?
Why you might get asked this:
This question assesses your ability to design systems that handle time-based data, manage memory efficiently, and support efficient queries for recent events. It tests data structure choices.
How to answer:
Use a circular queue or a TreeMap to store timestamps. For getHits, iterate and count hits within the last 300 seconds (5 minutes), removing expired ones.
Example answer:
Maintain a Queue storing timestamps. On hit(), add timestamp. On getHits(timestamp), remove all timestamps from the queue that are older than timestamp - 300 before returning the queue size. This ensures only relevant hits are counted.
2. How would you design Tic-Tac-Toe?
Why you might get asked this:
This question evaluates your object-oriented design skills and ability to manage game state, implement win conditions, and optimize for quick checks.
How to answer:
Represent the board with a 2D array. Use separate arrays/counters for rows, columns, and diagonals to track player moves, updating them after each move for O(1) win checking.
Example answer:
Initialize rows, cols arrays of size n, and diag, anti_diag counters. When player p moves at (r, c), add p (or -p for player 2) to rows[r], cols[c], etc. Check if any sum equals n (or -n).
3. How do you convert a string to an integer (atoi)?
Why you might get asked this:
This problem tests string parsing, handling edge cases like whitespace, signs, non-digit characters, and integer overflow/underflow. It's a classic robustness test.
How to answer:
Trim whitespace, identify optional sign, iterate through digits, accumulating value. Implement checks for overflow at each step, returning Integer.MAXVALUE or Integer.MINVALUE if exceeded.
Example answer:
Skip leading whitespace. Check for '+' or '-' for sign. Iterate digits, result = result * 10 + digit. Before adding, check if (result > MAX/10 || (result == MAX/10 && digit > 7)), or similarly for min, return boundary.
4. What is the maximum amount you can rob from houses in a row?
Why you might get asked this:
This is a standard Dynamic Programming (DP) problem, assessing your ability to identify overlapping subproblems and optimal substructure, fundamental to many optimization tasks.
How to answer:
Use DP. For each house i, dp[i] is the maximum money robbed up to house i. dp[i] = max(dp[i-1], dp[i-2] + nums[i]). Base cases: dp[0]=nums[0], dp[1]=max(nums[0], nums[1]).
Example answer:
Define dp[i] as the maximum money from houses 0 to i. dp[i] is either dp[i-1] (skip current house) or dp[i-2] + nums[i] (rob current house). The maximum of these is your solution for dp[i].
5. What is the maximum amount you can rob from houses in a circle?
Why you might get asked this:
This extends the basic House Robber problem, testing your ability to adapt a DP solution to handle cyclic constraints. It requires careful case analysis.
How to answer:
The circular arrangement means the first and last houses cannot both be robbed. Solve the problem twice: once excluding the last house, and once excluding the first house. The answer is the maximum of these two results.
Example answer:
Apply the House Robber I solution. Calculate max(rob(houses[0...n-2]), rob(houses[1...n-1])). Handle the edge case of a single house separately by returning its value.
6. How do you convert an IP address to CIDR blocks?
Why you might get asked this:
This question assesses your understanding of bit manipulation, network addressing concepts, and algorithmic thinking to break down a range into optimal CIDR blocks.
How to answer:
Convert the start IP to a 32-bit integer. Iterate, finding the largest CIDR block (smallest prefix length) that starts at the current IP and fits within the remaining range. Increment the IP by the block size.
Example answer:
Convert IP to long. While range remains, find k (prefix length) such that startip is 0 at its k least significant bits and startip + 2^(32-k) - 1 is less than or equal to endip. Add startip/k to result.
7. How do you find the median of two sorted arrays?
Why you might get asked this:
This hard problem tests binary search on answer space, requiring you to find a split point that partitions both arrays such that the left halves contribute to the median.
How to answer:
Use binary search on the smaller array to find a partition i such that (i elements from nums1 and j elements from nums2) correctly form the left half. Adjust j based on total length.
Example answer:
Perform binary search on nums1 for cut1. Calculate cut2 = (m+n+1)/2 - cut1. Ensure nums1[cut1-1] <= nums2[cut2] and nums2[cut2-1] <= nums1[cut1]. Adjust cut1 based on violations.
8. How do you find the longest substring without repeating characters?
Why you might get asked this:
A classic sliding window problem, this evaluates your ability to use two pointers and a hash set/map to efficiently track character occurrences and update window bounds.
How to answer:
Use a sliding window (left, right pointers) and a HashSet to store characters in the current window. Expand right, adding characters. If a duplicate is found, shrink left until the duplicate is removed.
Example answer:
Initialize left=0, maxLength=0, and a HashSet charSet. Iterate right from 0 to n-1. If charSet.contains(s.charAt(right)), remove s.charAt(left) and increment left until no duplicates. Add s.charAt(right) and update maxLength.
9. How do you merge overlapping intervals?
Why you might get asked this:
This tests your sorting algorithms and greedy approach. It's a common problem in scheduling and resource management.
How to answer:
Sort the intervals by their start times. Iterate through the sorted intervals, merging the current interval with the previous one if they overlap. Otherwise, add the previous interval to the result.
Example answer:
Sort intervals by interval[0]. Initialize merged list with first interval. Iterate from second interval: if current.start <= merged.last.end, update merged.last.end = max(merged.last.end, current.end). Else, add current to merged.
10. How do you find the shortest word ladder?
Why you might get asked this:
This hard problem tests graph traversal (BFS), string manipulation, and potentially pre-processing for efficiency. It models shortest path in an unweighted graph.
How to answer:
Build a graph where words differ by one character are connected. Use BFS starting from beginWord to find the shortest path to endWord, keeping track of the level.
Example answer:
Create a Queue for BFS (word, level). Add (beginWord, 1). Use a HashSet for visited words. For each word, generate all one-character-different transformations. If a transformation is in wordList and not visited, add to queue.
11. How do you find the K-th largest element in an array?
Why you might get asked this:
This problem assesses your knowledge of sorting, heaps (priority queues), or quickselect partitioning, all efficient ways to find order statistics.
How to answer:
Use a min-heap of size k. Iterate through the array; add elements to the heap. If heap size exceeds k, remove the smallest element. The top of the heap is the K-th largest.
Example answer:
Initialize a PriorityQueue (min-heap). For each num in nums, pq.offer(num). If pq.size() > k, pq.poll(). After iterating, pq.peek() is the K-th largest. QuickSelect offers O(N) average time.
12. How do you serialize and deserialize a binary tree?
Why you might get asked this:
This problem tests your understanding of tree traversals (DFS/BFS) and how to represent tree structure in a flat format, then reconstruct it.
How to answer:
For serialization, use a pre-order traversal (DFS) and append node values, using a sentinel (e.g., "#") for nulls. For deserialization, parse the string using a queue or index, recursively building nodes.
Example answer:
Serialize: preorder(node, sb). If node is null, append "#,". Else, append node.val + ",", then preorder(node.left), preorder(node.right). Deserialize: Split string by comma. Use a helper function that takes Queue and recursively builds nodes.
13. How do you count smaller numbers after self in an array?
Why you might get asked this:
This hard problem often requires advanced data structures like a Fenwick Tree (BIT) or Segment Tree, or a modified merge sort, to achieve efficient counting.
How to answer:
Modify merge sort: when merging two sorted halves, if an element from the right half is smaller than an element from the left half, it means all remaining elements in the left half are larger.
Example answer:
Augment merge sort to count inversions. When merging, if arr[i] > arr[j] (where i is left half, j is right half), then arr[j] is smaller than all remaining elements in the left half starting from i. Accumulate counts.
14. How would you design an LRU Cache?
Why you might get asked this:
This design question tests your understanding of combining data structures (hash map and doubly linked list) to achieve O(1) average time complexity for get and put operations while maintaining LRU order.
How to answer:
Use a HashMap to store key-node mappings for O(1) access. Use a DoublyLinkedList to maintain the order of usage, with the most recently used at the head and least recently used at the tail.
Example answer:
HashMap, Node with key, value, prev, next. head and tail sentinels for DLL. On get(key), move node to front. On put(key, value), if new, add to map and front; if full, remove tail.prev.
15. How do you validate a Binary Search Tree?
Why you might get asked this:
A fundamental tree problem, it tests your understanding of BST properties and recursion, requiring you to maintain a valid range for node values during traversal.
How to answer:
Perform a DFS (pre-order or in-order) traversal, passing min and max bounds for each node. For a node, its value must be greater than min and less than max. Recursively call for children with updated bounds.
Example answer:
isValidBST(node, min, max): if node is null, return true. If node.val <= min or node.val >= max, return false. Return isValidBST(node.left, min, node.val) AND isValidBST(node.right, node.val, max).
16. How do you determine if course schedules are possible?
Why you might get asked this:
This problem involves graph theory, specifically detecting cycles in a directed graph using DFS or BFS (topological sort), a common pattern in dependency resolution.
How to answer:
Represent courses and prerequisites as a directed graph. Use topological sort (either Kahn's algorithm with BFS or DFS with cycle detection) to determine if a valid ordering exists. A cycle implies it's impossible.
Example answer:
Build adjacency list and inDegree array. Add nodes with inDegree=0 to queue. While queue not empty, pop node, decrement inDegree of its neighbors. If neighbor inDegree becomes 0, add to queue. If all nodes processed, possible.
17. How do you count the number of islands in a grid?
Why you might get asked this:
A classic grid traversal problem, this assesses your ability to use DFS or BFS to explore connected components and mark visited cells to avoid recounting.
How to answer:
Iterate through the grid. If you find a '1' (land), increment the island count, then perform a DFS or BFS from that cell to mark all connected '1's as visited (e.g., change to '0') to prevent recounting.
Example answer:
Loop (r, c) through grid. If grid[r][c] == '1', increment count, then call dfs(grid, r, c) to sink the island (mark visited cells as '0'). dfs explores 4 directions recursively.
18. How do you calculate product of array except self?
Why you might get asked this:
This problem tests efficient array manipulation and avoiding division. It often leads to a two-pass approach using prefix and suffix products.
How to answer:
Create an output array. First pass: fill it with prefix products from left. Second pass: iterate from right, multiplying by suffix products and updating output. No division allowed.
Example answer:
res array. First pass: res[i] = product of nums[0...i-1]. tempproduct = 1. res[0]=1. For i=1...n-1, res[i] = tempproduct nums[i-1]. Second pass: temp_product = 1. For i=n-1...0, res[i] = res[i] tempproduct, tempproduct = temp_product * nums[i].
19. How do you find the maximum subarray sum?
Why you might get asked this:
A foundational DP problem (Kadane's Algorithm), it assesses your ability to find an optimal substructure and solve efficiently in O(N) time.
How to answer:
Use Kadane's algorithm: maintain currentmax (max sum ending at current position) and globalmax. currentmax = max(nums[i], currentmax + nums[i]). globalmax = max(globalmax, current_max).
Example answer:
Initialize maxSoFar = nums[0], maxEndingHere = nums[0]. Iterate from i=1: maxEndingHere = max(nums[i], maxEndingHere + nums[i]). maxSoFar = max(maxSoFar, maxEndingHere). Return maxSoFar.
20. How do you find the Kth smallest element in a BST?
Why you might get asked this:
This problem tests your understanding of Binary Search Tree properties and tree traversals, particularly in-order traversal which visits nodes in sorted order.
How to answer:
Perform an in-order traversal (DFS) of the BST. The Kth element visited during an in-order traversal will be the Kth smallest element. Stop once k elements are counted.
Example answer:
Recursive in-order traversal: inorder(node). Recursively call inorder(node.left). Decrement k. If k is 0, return node.val. Then call inorder(node.right). Non-recursive using stack is also common.
21. How do you search for a word in a grid?
Why you might get asked this:
This is a classic backtracking problem on a grid. It tests recursive problem-solving, exploring all paths, and marking visited cells to avoid cycles.
How to answer:
For each cell in the grid, start a DFS. The DFS function tries to match the current character of the word. If matched, recursively try to match the next character in 4 directions (up, down, left, right), marking current cell visited.
Example answer:
dfs(board, word, r, c, index): Base case: index == word.length(), return true. Check bounds and board[r][c] == word[index]. Mark board[r][c] as visited (e.g., change to '#'). Recursively call dfs for 4 neighbors. Backtrack: restore board[r][c].
22. How do you find duplicate subtrees in a binary tree?
Why you might get asked this:
This problem tests tree serialization, hashing, and map usage to identify identical tree structures. It combines tree traversal with efficient lookup.
How to answer:
Use a post-order traversal to serialize each subtree (e.g., left-subtree-str,right-subtree-str,root-val). Store these serialized strings in a HashMap with their counts. If count > 1, add the root to result.
Example answer:
serialize(node, map, result): Base case for null. Recursively call for left and right. Combine results as leftstr + "," + rightstr + "," + node.val. Store this string in map. If map.get(str) == 2, add node to result. Return str.
23. How do you find the top K frequent elements?
Why you might get asked this:
This problem combines frequency counting (hash map) with efficient retrieval of top elements (heap/priority queue or bucket sort).
How to answer:
First, use a HashMap to count frequencies of all elements. Then, use a min-heap of size k to store pairs of (frequency, element). Iterate through map entries, adding to heap and removing smallest if heap size exceeds k.
Example answer:
Count frequencies into Map freqMap. Create PriorityQueue> (min-heap) ordered by value (frequency). Iterate freqMap.entrySet(): pq.offer(entry). If pq.size() > k, pq.poll(). Extract keys from pq.
24. How do you find the sliding window maximum?
Why you might get asked this:
This hard problem tests your ability to use a deque (double-ended queue) to maintain a monotonically decreasing sequence of indices, optimizing window maximum calculations to O(N).
How to answer:
Use a Deque to store indices of elements in the current window in decreasing order of their values. When moving the window, remove old indices. When adding new, remove smaller elements from back of deque.
Example answer:
Initialize Deque dq. Iterate i from 0 to n-1. Remove indices from dq's front if they are outside current window (i - k). Remove indices from dq's back if nums[i] is greater than nums[dq.peekLast()]. Add i to dq. result[i-k+1] = nums[dq.peekFirst()].
25. How do you implement a Trie (Prefix Tree)?
Why you might get asked this:
This design problem tests your understanding of tree-like data structures optimized for string prefix searching. It's fundamental for autocomplete and dictionary applications.
How to answer:
Design a TrieNode class (children array/map, isEndOfWord boolean). Implement insert, search, and startsWith methods by traversing the trie, creating nodes as needed.
Example answer:
TrieNode has TrieNode[] children = new TrieNode[26] and boolean isEndOfWord. insert(word) iterates chars, creating new nodes if children[char-'a'] is null. search(word)/startsWith(prefix) traverse similar paths.
26. How do you find the maximum sum rectangle in a 2D matrix?
Why you might get asked this:
This hard dynamic programming problem extends Kadane's algorithm to two dimensions, requiring you to iterate through all possible column pairs.
How to answer:
Iterate through all possible pairs of left and right columns. For each pair, calculate a 1D array of row sums within these columns. Apply Kadane's algorithm to this 1D array to find the max subarray sum, which represents the max sum rectangle.
Example answer:
Outer loops for leftcol and rightcol. Inner loop iterates through rows, calculating rowsum[r] = sum(matrix[r][leftcol...rightcol]). Apply Kadane's algorithm on rowsum array to find max currentsum and update maxoverall_sum.
27. How do you implement a Median Finder?
Why you might get asked this:
This hard problem tests your ability to maintain sorted order and efficiently retrieve the median from a data stream using two heaps (min-heap and max-heap).
How to answer:
Use two heaps: a max-heap for the smaller half of numbers and a min-heap for the larger half. Ensure the sizes are balanced (differ by at most 1). The median is then the top of the larger heap, or the average of the two tops.
Example answer:
maxHeap stores smaller half, minHeap stores larger half. On addNum(num): add to maxHeap. Move maxHeap.top to minHeap. If maxHeap.size < minHeap.size, move minHeap.top to maxHeap. findMedian() returns maxHeap.top or average.
28. How would you design a concurrent Logger Rate Limiter?
Why you might get asked this:
This concurrency problem assesses your ability to handle shared state safely in a multithreaded environment, typically using synchronization mechanisms like mutexes or concurrent data structures.
How to answer:
Use a ConcurrentHashMap to store (message, timestamp). Implement shouldPrintMessage with proper synchronization (e.g., synchronized block or ReentrantLock) to ensure atomic updates and checks for rate limits.
Example answer:
ConcurrentHashMap lastPrintTime. shouldPrintMessage(timestamp, message): if message not in map or timestamp >= lastPrintTime.get(message) + 10, then update map and return true. Use putIfAbsent or computeIfPresent for atomic ops.
29. How would you implement a Thread-Safe Bounded Blocking Queue?
Why you might get asked this:
This problem is a cornerstone of concurrency, testing your understanding of producer-consumer patterns, condition variables, and mutexes to manage shared resources safely.
How to answer:
Use a fixed-size ArrayDeque or LinkedList as the underlying queue. Protect access with a ReentrantLock and manage empty and full conditions using Condition variables (notEmpty, notFull).
Example answer:
Lock lock = new ReentrantLock(); Condition notEmpty = lock.newCondition(); Condition notFull = lock.newCondition(); enqueue: lock.lock(); try { while (isFull) notFull.await(); add item; notEmpty.signalAll(); } finally { lock.unlock(); } dequeue similar.
30. How would you implement a Concurrent Counter?
Why you might get asked this:
This basic concurrency question tests your knowledge of atomic operations or synchronization primitives to ensure thread-safe increments/decrements.
How to answer:
Use java.util.concurrent.atomic.AtomicInteger for non-blocking, thread-safe operations, or synchronize access to a simple int counter using synchronized methods or blocks.
Example answer:
Option 1 (Atomic): private final AtomicInteger count = new AtomicInteger(0); public void increment() { count.incrementAndGet(); } Option 2 (Synchronized): private int count = 0; public synchronized void increment() { count++; }
Other Tips to Prepare for a Databricks Interview
Beyond mastering these common Databricks interview questions, holistic preparation is key. As Abraham Lincoln once said, "Give me six hours to chop down a tree and I will spend the first four sharpening the axe." Similarly, invest time in sharpening your fundamental skills. Practice a variety of problems focusing on binary search, which is a recurring pattern in Databricks' interviews, and ensure you're comfortable with advanced data structures like tries and segment trees. Crucially, dedicate significant time to concurrency and multithreading problems, as Databricks frequently includes a dedicated round on this topic. Understanding system design at a high level, especially for distributed data processing environments relevant to Databricks, will also set you apart. Be ready to discuss your past projects, showcasing your experience with big data tools and your thought process. For targeted practice and real-time feedback, consider using tools like Verve AI Interview Copilot. It can simulate Databricks interview scenarios, helping you refine your technical explanations and approach. Visit https://vervecopilot.com to enhance your preparation with personalized insights from Verve AI Interview Copilot, making sure you're fully confident for your Databricks interview.
Frequently Asked Questions
Q1: How long are Databricks interview rounds?
A1: Technical rounds at Databricks typically last 45-60 minutes, with a strong focus on coding problems and their optimization.
Q2: What programming languages are preferred at Databricks?
A2: While you can usually use your preferred language (Java, Python, C++, Go), proficiency in Python or Scala is highly valued given Databricks' ecosystem.
Q3: Is system design always part of the Databricks interview?
A3: System design is usually a round for experienced roles, assessing your ability to design scalable distributed systems relevant to Databricks' products.
Q4: How important are behavioral questions for Databricks?
A4: Behavioral questions are important for Databricks to assess cultural fit and how you handle teamwork, challenges, and learning.
Q5: Should I focus on LeetCode hard problems for Databricks?
A5: Yes, a significant portion of Databricks' technical questions are LeetCode medium to hard, emphasizing optimal solutions and edge cases.
