Databricks Interview Questions (11+ Questions)

Last Updated: June 8, 2026 β€’ 11 Questions β€’ Real Company Interviews

Prepare for your Databricks interview with our comprehensive collection of 11+ real interview questions and detailed answers. These questions have been curated from actual Databricks technical interviews across various roles including DevOps Engineer, Data Engineer, QA Engineer, and more.

11
Interview Questions
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Categories
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Difficulty Levels

Table of Contents

Our Databricks interview questions cover a wide range of technical topics and difficulty levels, from entry-level positions to senior roles. Each question includes detailed explanations and answers to help you understand the concepts and prepare effectively for your interview.

πŸ’‘ Pro Tips for Databricks Interviews

  • Practice each question and understand the underlying concepts
  • Review Databricks's specific technologies and methodologies
  • Prepare follow-up questions and edge cases
  • Practice explaining your solutions clearly and concisely

Interview Questions & Answers

1. Investigate Mounted Disk Usage

Company: Databricks Difficulty: medium πŸ”’ Premium Categories: Devops

Learn how to diagnose and resolve disk space exhaustion issues on mounted volumes using Linux Bash commands. This guide covers checking filesystem usage, identifying largest files, freeing storage space, and verifying recovery, essential for troubleshooting storage capacity problems, preventing service failures, and maintaining application availability.

2. Connect Isolated Network Namespaces

Company: Databricks Difficulty: medium πŸ”’ Premium Categories: Devops

Configure Linux network namespaces and bridges for isolated container networking. Learn to create separate network segments with veth pairs, interconnect namespaces using Linux bridges, enable inter-namespace communication, and verify connectivity. This guide covers network namespace isolation, virtual ethernet configuration, bridge setup, IP forwarding, and routing between isolated network stacks. Essential for container networking troubleshooting, microservices development, understanding Docker/Kubernetes networking, and implementing custom network topologies in production environments.

3. Two Sum II - Input Array Is Sorted

Company: Databricks Difficulty: medium Categories: Devops, Data engineering, Quality assurance

def two_sum(numbers: list[int], target: int) -> list[int]:
l, r = 0, len(numbers) - 1

while l < r:
    cur_sum = numbers[l] + numbers[r]

    if cur_sum > target:
        r -= 1
    elif cur_sum < target:
        l += 1
    else:
        return [l + 1, r + 1]
        
return []

4. Secure Credential Rotation with Secrets Manager

Company: Databricks Difficulty: hard πŸ”’ Premium Categories: Devops

Implement a secure, automated credential-rotation flow using Secrets Manager, KMS, Lambda, SSM, SNS, and CloudWatch Logs with least-privilege IAM.

5. Analyze Sales Dataset Dimensions and Calculate Total Revenue

Company: Databricks Difficulty: easy Categories: Data analysis, Data engineering

Load a sales CSV file with pandas, calculate dataset dimensions and cell count, classify data size using thresholds, and compute total revenue from quantity and price columns.

6. Broadcast Join

Company: Databricks Difficulty: easy Categories: Data analysis, Data engineering

Join a large orders table with a small customers table using a broadcast join and verify it from the execution plan.

7. Flooring Company Data

Company: Databricks Difficulty: medium Categories: Data analysis, Data engineering

SELECT
o.order_id,
o.customer_id,
SPLIT_PART(c.full_name, ' ', 1) AS first_name,
SPLIT_PART(c.full_name, ' ', 2) AS last_name,
c.location,
o.product_id,
SPLIT_PART(p.product_info, ',', 1) AS product_type,
SPLIT_PART(p.product_info, ',', 2) AS product_color,
o.quantity
FROM {{ ref("orders") }} AS o
INNER JOIN {{ ref("customers") }} AS c
ON o.customer_id = c.customer_id
INNER JOIN {{ ref("products") }} AS p
ON o.product_id = p.product_id

8. Analyzing Self-Interactions on Social Media

Company: Databricks Difficulty: easy Categories: Data analysis, Data engineering

Master data filtering and aggregation in PySpark. Learn how to filter rows by comparing two columns against each other, rename columns during a GroupBy operation, and count interaction occurrences.

9. Calculate Average Delivery Time

Company: Databricks Difficulty: medium πŸ”’ Premium Categories: Data analysis, Data engineering

Objective

To answer the interview question regarding SQL, you need to write an SQL query that calculates the average number of days taken to deliver orders after they have been shipped. Only orders with both a shipping date and a delivery date recorded should be included in the calculation.

###...


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10. Cross-Sell Opportunity Identifier

Company: Databricks Difficulty: medium πŸ”’ Premium Categories: Data engineering

Detailed Explanation for SQL Interview Question on Unpurchased Product Categories

Objective

Write an SQL query to determine which product categories have not been purchased by each customer. The query should return a list of customers along with the categories they have not purchased, sor...


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11. E-commerce Marketplace API Testing

Company: Databricks Difficulty: medium πŸ”’ Premium Categories: Quality assurance

Amazon operates the world's largest e-commerce marketplace with over 300 million active customers and 12 million products. QA testing of Amazon marketplace APIs requires comprehensive validation of product search, cart management, order processing, and inventory tracking to ensure reliable shopping ...


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