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
Medium
Software Engineer Locked

Partition a Target String by Source Substrings

This question evaluates understanding of string algorithms and optimal partitioning, specifically substring matching and minimizing the number of cont...

Coding & Algorithms
3
0
19 people solved
May 14, 2026
Databricks logo
Databricks
Medium
Machine Learning Engineer Locked

Design Harmful Content and OOM Detection

This question evaluates machine learning system-design competencies, including safety-oriented content classification, taxonomy and labeling strategy,...

ML System Design
5
0
90 people solved
Jan 6, 2026
Databricks logo
Databricks
Medium
Software EngineerIntern

Design Tic-Tac-Toe and QPS data structures

You are given two independent coding problems that focus on data structure and API design. --- Problem 1: Generalized Tic-Tac-Toe Game with Simple AI ...

Coding & Algorithms
51
0
369 people solved
Nov 13, 2025
Databricks logo
Databricks
Medium
Data Engineer

Diagnose data quality and pipeline performance issues

Diagnose data quality and pipeline performance issues Scenario You are interviewing for a Data Solutions Architect role. A customer is using a cloud d...

Behavioral & Leadership
15
0
135 people solved
Jul 25, 2025
Databricks logo
Databricks
Medium
Software Engineer

Implement a rate-limited hit counter

You are designing a hit counter that records the number of hits received in the past 5 minutes. Implement a class HitCounter with the following method...

Coding & Algorithms
20
0
327 people solved
Dec 8, 2025
Databricks logo
Databricks
Hard
Data ScientistIntern

Diagnose and fix multicollinearity in income regression

You want to estimate the relationship between gender and income using a regression model, while controlling for other covariates such as: - age - educ...

Statistics & Math
5
0
79 people solved
Oct 14, 2025
Databricks logo
Databricks
Medium
Software Engineer

Implement Random Connectivity and Grid Routing

This entry contains two independent coding problems. Problem 1: Uniformly connect node groups You are given k non-empty, disjoint groups of unique nod...

Coding & Algorithms
5
1
54 people solved
Apr 17, 2026
Databricks logo
Databricks
Medium
Software EngineerSenior+

Find optimal commute mode in a city graph

You are designing a route planner that suggests the best way to commute between two points in a city using different transportation modes. The city is...

Coding & Algorithms
30
0
284 people solved
Oct 19, 2025
Databricks logo
Databricks
Hard
Data Scientist

Test coin fairness from 560 tails in 1000 flips

You flip a coin n = 1000 times and observe 560 tails. At significance level α = 0.05, test whether the coin is fair. - State the null and alternative ...

Statistics & Math
7
0
65 people solved
Dec 12, 2025
Databricks logo
Databricks
Hard
Software EngineerNew Grad

Implement firewall matching with CIDR rules

Implement a simple IPv4 firewall rule matcher. Problem You are given an ordered list of firewall rules. Each rule has: - an action: ALLOW or DENY - a ...

Coding & Algorithms
11
1
177 people solved
Dec 10, 2025
Databricks logo
Databricks
Hard
Data ScientistSenior+ Locked

Find Top-5 Similar Rows

This question evaluates pairwise similarity matching, numerical feature comparison using mean squared error, null-value exclusion, and deterministic t...

Data Manipulation (SQL/Python)
9
0
67 people solved
Mar 5, 2026
Databricks logo
Databricks
Hard
Software Engineer Locked

Minimize run-length encoded length with deletions

This question evaluates proficiency in string manipulation, run-length encoding concepts, and dynamic programming-based optimization under deletion co...

Coding & Algorithms
29
0
502 people solved
Feb 12, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Choose fastest transportation mode on city grid

This question evaluates a candidate's ability to model constrained grid movement and perform weighted shortest-path optimization, covering skills in g...

Coding & Algorithms
16
0
188 people solved
Feb 11, 2026
Databricks logo
Databricks
Medium
Machine Learning EngineerSenior+ Locked

Delete a Character From Cover

This question evaluates proficiency in string manipulation, interval arithmetic, and data-structure updates with attention to index mapping and edge-c...

Coding & Algorithms
11
0
82 people solved
Feb 7, 2026
Databricks logo
Databricks
Medium
Software EngineerSenior+

Implement RLE and bit-packing compression

You are asked to implement two related compression/decompression schemes: Run-Length Encoding (RLE) and bit-packing. --- Part 1 — Run-Length Encoding ...

Coding & Algorithms
34
1
238 people solved
Oct 10, 2025
Databricks logo
Databricks
Hard
Software EngineerNew Grad Locked

Evaluate ACL rules for IP and CIDR

This question evaluates a candidate's competency in IP address and CIDR arithmetic, bitwise reasoning, and algorithm design within the Coding & Algori...

Coding & Algorithms
9
0
62 people solved
Jan 22, 2026
Databricks logo
Databricks
Hard
Software Engineer Locked

Find path between nodes in Fibonacci tree

This question evaluates understanding of recursive tree structures, preorder indexing, implicit tree representations, algorithmic reasoning about node...

Coding & Algorithms
4
0
42 people solved
Jan 22, 2026
Databricks logo
Databricks
Medium
Software Engineer

Design IP/CIDR rule matcher

Design and implement a rule matcher that returns 'accept' or 'deny' for a given IPv4 address based on a set of rules. Each rule can be either an inclu...

Coding & Algorithms
26
0
253 people solved
Sep 6, 2025
Databricks logo
Databricks
Hard
Software Engineer Locked

Find all anagram start indices

This question evaluates proficiency in string manipulation, algorithmic efficiency, and pattern detection through character frequency analysis, assess...

Coding & Algorithms
14
0
137 people solved
Jan 7, 2026
Databricks logo
Databricks
Hard
Software Engineer Locked

Implement a Tic-Tac-Toe game API

This question evaluates implementation skills in data structures and algorithmic optimization, focusing on efficient state tracking and constant-time ...

Coding & Algorithms
14
0
101 people solved
Jan 7, 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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