Databricks - interview guide

Databricks interview questions - a complete guide

Databricks' technical rounds are reported to be hard (LeetCode hard level), and an understanding of large data systems is checked too. Here is how to prepare for both.

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About Databricks

Databricks builds a data and AI platform - it was started by the creators of Apache Spark. Companies store and process large data and train AI models on it. It has an engineering centre in Bengaluru.

This guide covers 13 Databricks interview questions with answers - what each question checks and how to answer it - plus the Databricks interview process, round by round, a 7-day preparation plan for freshers, tips and the mistakes to avoid.

Databricks hiring process

  1. Shortlisting

    Reports say CGPA, competitive programming rating and projects are looked at.

  2. 3 technical rounds

    DSA (often hard), implementation and distributed systems concepts.

  3. HR / hiring manager

    Motivation and fit.

Databricks does not publish its India fresher interview process in detail (as of our check). This is based on candidate reports - many of them for experienced roles, so the fresher process can differ. Confirm with the recruiter.

Sources (checked 16 October 2026): Databricks Careers. Hiring processes change - read the official notice before you apply.

Databricks interview questions and answers

DSA and implementation

  1. 1.Build a key-value store with snapshots: put, get, snapshot() and get(key, snapshot_id).

    What they're checking
    Versioning - a core idea of data systems.
    How to answer
    Keep a sorted list of (snapshot_id, value) for each key. snapshot() just increments a counter. For an old value, binary search that list for the last entry with snapshot_id less than or equal to the one asked.

  2. 2.How would you find the 10 most frequent words in a file larger than memory?

    What they're checking
    Thinking in external memory.
    How to answer
    While reading, split words into N small files by hash(word) % N - a word always lands in the same file. Count each file separately and take its top 10, then the overall top 10 with a heap.

  3. 3.Find a path between two nodes of a graph whose heaviest edge is as light as possible.

    What they're checking
    Union-Find / MST thinking.
    How to answer
    Sort edges by weight and join them with Union-Find; the edge at which both nodes become connected is the answer. Or binary search on the answer + BFS.

  4. 4.Range sum queries and point updates on an array - both fast.

    What they're checking
    Fenwick tree / segment tree.
    How to answer
    A Fenwick tree makes both update and prefix sum O(log n); range sum = prefix(r) - prefix(l-1). A segment tree works too - say when to use which.

  5. 5.Build an iterator that flattens a nested list lazily.

    What they're checking
    Iterator design.
    How to answer
    Keep iterators on a stack; in hasNext(), until the top yields an integer, push the iterator of any list found and pop finished ones. Don't flatten the whole list in advance.

Data and distributed systems

  1. 6.What is the difference between a transformation and an action in Spark? Why lazy evaluation?

    What they're checking
    The foundation of Databricks.
    How to answer
    Transformations (map, filter) only build a plan; actions (count, collect, write) run the work. Being lazy lets Spark see the whole plan and optimise it - such as filtering first.

  2. 7.Why is a columnar format like Parquet fast for analytics queries?

    What they're checking
    Data formats.
    How to answer
    A query reads only the columns it needs, similar values in a column compress well, and min/max stats let whole chunks be skipped.

  3. 8.In a join, one key holds 90% of the data (skew) - what do you do?

    What they're checking
    A real performance problem.
    How to answer
    If one table is small, a broadcast join. Otherwise add a random 'salt' to the heavy key to split it into several parts, and copy the other side that many times.

  4. 9.How would you spread data over 100 machines while machines keep being added and removed?

    What they're checking
    Partitioning.
    How to answer
    With simple hash % N, almost all data moves when a machine changes. With consistent hashing, machines and keys sit on a ring - when a machine leaves, only its keys move; virtual nodes keep it balanced.

  5. 10.What does 'exactly-once' mean in distributed processing?

    What they're checking
    Correctness.
    How to answer
    Each record affects the result exactly once, even with retries. Usually achieved with 'at-least-once delivery + idempotent writes' or a transactional commit.

Behavioural

  1. 11.Explain the hardest competitive programming problem you have solved.

    What they're checking
    The real understanding behind a CP rating.
    How to answer
    The problem, why your first approach failed, which idea worked, and the complexity - ready for the interviewer's follow-ups.

  2. 12.When did you improve something's performance a lot?

    What they're checking
    A data/performance mindset.
    How to answer
    What you measured first (profiling), what the bottleneck was, what you changed and how big the difference was - with numbers.

  3. 13.Why Databricks - what excites you about data and AI?

    What they're checking
    Motivation.
    How to answer
    Your work or study in Spark/lakehouses or AI infrastructure - specifically.

7-day preparation plan for the Databricks interview

  1. Day 1: Union-Find and graphs (MST, shortest path variants).
  2. Day 2: Fenwick and segment trees.
  3. Day 3: Design-style implementation - a snapshot KV store, iterators.
  4. Day 4: Spark basics - transformations, actions, joins, skew.
  5. Day 5: Distributed systems - partitioning, exactly-once.
  6. Day 6: Two timed hard problems + explain your CP problems.
  7. Day 7: Behavioural stories + a mock.

Preparation tips

  • Hard-level DSA - graphs, Union-Find, segment/Fenwick trees, binary search on the answer.
  • Understand Spark basics and columnar formats once.
  • If you have a CP profile, practise explaining your best problems.
  • Write clean, testable code in implementation problems.

Mistakes to avoid

  • Preparing only up to medium problems.
  • Listing Spark on your resume without knowing the basics.
  • Not even stating the brute force on a hard problem - simple first, then better.

Frequently asked questions

How hard is the Databricks interview?

According to reports, the technical rounds have LeetCode hard-level questions.

How does Databricks shortlist candidates?

Reports mention CGPA, competitive programming rating and projects.

Do I need to know Spark for the Databricks interview?

Not for every role, but a basic understanding of data systems makes a good impression.

Where is Databricks in India?

An engineering centre in Bengaluru is reported.

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