Databricks Interview Questions

Databricks Interview Questions

Practice 131 real Databricks interview questions for 2026. Covers all top categories — Coding & Algorithms, System Design, Behavioral & Leadership, Statistics & Math, Software Engineering Fundamentals — across Software Engineer, Data Scientist, Machine Learning Engineer, and Data Engineer roles. Real questions from actual interviews with detailed solutions; this collection explains what’s distinctive about Databricks interviews, what interviewers evaluate, what to expect in each round, and how to structure your interview preparation. Expect a heavy software-engineering tilt: Software Engineer rounds repeatedly probe storage and data-platform design (hierarchical file systems, cache designs, lakehouse tradeoffs), distributed job scheduling and dependency-aware pipelines, concurrency and multithreaded systems (synchronous log writers, thread-safe KV stores), and performance-focused algorithmic problems and query optimizations. Data Scientist questions emphasize regression assumptions and coefficient transformations, similarity search across datasets, hypothesis testing and metric tradeoffs (ROC-AUC vs PR-AUC), and product-analytics counting problems. Machine Learning Engineer prompts focus on implementing algorithms (gradient descent, lazy arrays) and safety/OOM detection for models. Data Engineer coverage targets data-quality diagnostics, Spark/partitioning and pipeline performance. To prepare, prioritize timed coding practice, system-design sketches that show tradeoffs for data systems, concise statistics explanations, and polished STAR stories for behavioral rounds.

131 Questions 1 Company07.30.2026
Showing 20 results
Role
Databricks logo
Databricks
Hard
Software Engineer Locked

Convert an IP range to minimal CIDRs

This question evaluates understanding of IPv4 address encoding, binary and bitwise operations, and optimal interval covering for expressing consecutiv...

Coding & Algorithms
6
0
48 people solved
Jan 7, 2026
Databricks logo
Databricks
Medium
Machine Learning Engineer Locked

Implement a Lazy Array

This question evaluates understanding of lazy evaluation and deferred computation, implementation of iterator/generator-like abstractions, and the abi...

Coding & Algorithms
1
0
12 people solved
Jan 6, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Implement lazy array and KV store

This question evaluates understanding of lazy evaluation and functional transformation chaining for a LazyArray alongside in-memory data structure des...

Coding & Algorithms
8
0
100 people solved
Jan 5, 2026
Databricks logo
Databricks
Medium
Software Engineer

Design an IP filter using CIDR rules

Explain CIDR notation with a couple of concrete examples. Show how to convert a prefix like 192.168.0.0/16 into an inclusive 32-bit integer range and ...

Coding & Algorithms
34
0
258 people solved
Jul 31, 2025
Databricks logo
Databricks
Easy
Data ScientistIntern Locked

Relate coefficients under linear feature transformation

This question evaluates understanding of linear regression parameter relationships under linear feature transformations, model equivalence and identif...

Statistics & Math
7
0
64 people solved
Dec 11, 2025
Databricks logo
Databricks
Hard
Data ScientistIntern

Relate coefficients under linear feature transformation

Suppose you are fitting a linear regression model and you consider two different feature parameterizations. Original features: x1, x2. Transformed fea...

Statistics & Math
7
0
54 people solved
Oct 14, 2025
Databricks logo
Databricks
Medium
Software Engineer

Implement a sliding-window hit counter

Implement a hit counter that supports recordHit(timestamp) and getHits(pastSeconds). Use a fixed-size array to maintain a sliding time window (e.g., l...

Coding & Algorithms
16
0
111 people solved
Sep 6, 2025
Databricks logo
Databricks
Medium
Software Engineer

Design BFS to detect forced win in Tic-Tac-Toe

You are given an n×n Tic-Tac-Toe–like board and a target k (1 ≤ k ≤ n). From the current board state and the player to move, design an algorithm to de...

Coding & Algorithms
18
0
166 people solved
Aug 13, 2025
Databricks logo
Databricks
Medium
Data Scientist

Explain Linear Regression Feature Transformation Equivalence

Explain Linear Regression Feature Transformation Equivalence Linear Regression Feature Representations and High-Dimensional Modelling Context You are ...

Machine Learning
8
0
76 people solved
Aug 4, 2025
Databricks logo
Databricks
Medium
Software Engineer Locked

IPv4 CIDR Range Membership Queries

This question evaluates a candidate's ability to design an efficient longest-prefix-match lookup structure for IP address ranges, a core networking an...

Coding & Algorithms
1
0
10 people solved
Jun 23, 2026
Databricks logo
Databricks
Medium
Software EngineerSenior+ Locked

Optimal Commute: Nearest Transit Distance in a City Grid

This question evaluates a candidate's ability to apply multi-source breadth-first search to a grid-based shortest-path problem. It tests graph travers...

Coding & Algorithms
1
0
21 people solved
Jun 9, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Choose the Best Travel Mode

Solve a grid-based pathfinding problem where a traveler must choose the transportation mode with the lowest travel time and cost tie-breakers. The que...

Coding & Algorithms
1
0
4 people solved
Jun 4, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Implement a Snapshot Set Iterator

This question evaluates data structure design, versioning, and iterator consistency concepts within the Coding & Algorithms domain, focusing on implem...

Coding & Algorithms
3
0
29 people solved
May 24, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Find the Best Commute Mode

This question evaluates competency in grid-based pathfinding, graph traversal, and multi-criteria optimization of path time and cost within the Coding...

Coding & Algorithms
3
0
21 people solved
May 17, 2026
Databricks logo
Databricks
Medium
Machine Learning EngineerSenior+

Design In-Memory QPS Counter

Implement an in-memory traffic counter. You need to support two operations for each logical key, such as an endpoint, customer, or service name: - rec...

Coding & Algorithms
12
0
93 people solved
Apr 18, 2026
Databricks logo
Databricks
Hard
Software Engineer Locked

Find Fastest Commute Mode

This question evaluates graph traversal and shortest-path reasoning on a constrained 2D grid, including modeling movement rules per transportation mod...

Coding & Algorithms
10
1
76 people solved
Apr 8, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Solve Grid Path and Graph Sampling

This question evaluates graph algorithms and probabilistic/combinatorial reasoning, covering multi-criteria shortest-path optimization (minimizing tra...

Coding & Algorithms
4
0
41 people solved
Apr 3, 2026
Databricks logo
Databricks
Medium
Backend Engineer

Find First Anagram Occurrence

Given two strings text and pattern, return the starting index of the first substring in text that is an anagram of pattern. If no such substring exist...

Coding & Algorithms
13
0
132 people solved
Apr 2, 2026
Databricks logo
Databricks
Hard
Software Engineer Locked

Find Path Between Fibonacci Tree Nodes

This question evaluates a candidate's ability to reason about recursive tree structures, map preorder indices to positions in a recursively defined Fi...

Coding & Algorithms
9
0
74 people solved
Feb 13, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Compute 5-minute rolling average load

This question evaluates understanding of sliding-window aggregation and efficient data-structure design for time-series event streams, focusing on com...

Coding & Algorithms
8
0
159 people solved
Feb 3, 2026

Frequently Asked Questions

How difficult are Databricks interviews?
Databricks interviews are generally high bar and tend to be more difficult than average because the company evaluates both deep algorithmic skill and domain-specific data-platform knowledge. For Software Engineer roles you should expect LeetCode-style algorithmic problems at medium-to-hard difficulty plus systems and concurrency prompts drawn from real platform work like cache design, job schedulers, multi-threaded log writers, and lazy data structures. Data Scientist interviews mix classical statistics and practical data-manipulation problems (linear-regression diagnostics, ROC vs PR tradeoffs, nearest-neighbor similarity), while ML Engineer and Data Engineer loops emphasize production ML and pipeline debugging respectively. Strong communication and tradeoff reasoning are required throughout.
What does the Databricks interview process look like and which roles and categories appear most often?
Databricks commonly uses a multi-stage process: recruiter screen, one or two technical screens (coding or ML/statistics), and a virtual onsite containing 3–5 interviews (coding, system/architecture, and behavioral/hiring-manager rounds). Coding and system-design questions dominate for Software Engineer candidates and often reference distributed-data themes such as Spark-style computation, Delta Lake consistency, job orchestration and storage/compute tradeoffs. Data Scientist loops focus on hypothesis testing, regression diagnostics, and scalable SQL or similarity searches. Machine Learning Engineer and Data Engineer interviews center on productionizing models, OOM and harmful-content detection, and diagnosing pipeline performance and data-quality issues.
What is a practical prep timeline before interviewing at Databricks?
A focused 6-week plan tends to work well: weeks 1–2 refresh core algorithms, data structures, and coding speed with timed problems; weeks 3–4 study distributed-data concepts, Spark/Delta basics, and system-design patterns relevant to job schedulers, caching, and storage-consistency tradeoffs; week 5 concentrate on role-specific skills—statistics and evaluation metrics for Data Scientists, gradient-descent and model-deployment for MLEs, pipeline debugging for Data Engineers; week 6 run full mock interviews, practice clear tradeoff explanations, and rehearse STAR behavioral stories showing impact and ownership. Adjust intensity based on experience and time available.
What key subtopics should I focus on for Databricks interviews?
Prioritize a short list that maps to the company’s product focus and the question breakdown: for engineers, algorithms, concurrency, and storage-oriented system design problems such as dependency-aware job schedulers, cache designs with hit-count tracking, multi-threaded log writers, and lazy-array/KV-store implementations; for data science, linear-regression assumptions, multicollinearity diagnosis, hypothesis testing, ROC-AUC vs PR-AUC tradeoffs, and nearest-neighbor similarity across datasets; for ML engineers, solid numerical optimization (gradient descent) and production safety (OOM and harmful-content detection); for data engineers, pipeline performance troubleshooting, partitioning, and data-quality diagnostics.
Any standout interview tips and common pitfalls to avoid at Databricks?
Show systems thinking: explain consistency, fault modes, cost/latency tradeoffs, and operational concerns (retries, backpressure, monitoring) when designing solutions. In coding rounds write clean, testable code and discuss complexity and edge cases. For data and ML questions, be explicit about metric choices, assumptions, and how data quality affects conclusions. Avoid common pitfalls like ignoring distributed constraints, skipping concurrency details, failing to justify design tradeoffs, or overfitting to toy metrics. Close each answer with a short summary of tradeoffs and next steps you would take in production to demonstrate ownership and pragmatism.

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