Top Databricks Interview Questions & Answers for 2025

 In 2025, the demand for Databricks professionals continues to rise as organizations adopt unified analytics platforms for handling massive data workloads. Whether you're preparing for your first Databricks role or aiming to level up your career, mastering key Databricks interview questions is essential. 

At AccentFuture, our Databricks online training equips learners with real-world skills and project experience, ensuring they are interview-ready for high-growth roles in data engineering, analytics, and AI. 

Below is a curated list of the top Databricks interview questions and answers for 2025, covering Apache Spark fundamentals, Delta Lake, notebooks, workspace usage, and advanced data engineering topics. 

 

1. What is Databricks and how does it differ from Apache Spark? 

Answer: 
 Databricks is a cloud-based unified analytics platform built by the creators of Apache Spark. While Spark is an open-source distributed computing engine, Databricks offers a managed environment with collaborative notebooks, scalable compute clusters, and built-in integrations with ML and BI tools. Databricks enhances Spark with features like Delta Lake for ACID transactions, MLflow, and simplified job orchestration. 

2. What is a Databricks Workspace? 

Answer: 
 A Databricks Workspace is an interactive environment where teams can create, manage, and share notebooks, libraries, dashboards, and jobs. It provides a user-friendly interface to manage clusters, run Spark code, and collaborate across languages like Python, SQL, Scala, and R. Workspaces improve productivity by combining development, version control, and deployment in one place. 

3. Explain the role of Delta Lake in Databricks. 

Answer: 
 Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It enables data reliability with schema enforcement, time travel, and versioning. In Databricks, Delta Lake powers data lakes with high performance and data integrity, making it ideal for real-time streaming and batch pipelines. 

4. What is the purpose of the Databricks Job Scheduler? 

Answer: 
 The Job Scheduler automates the execution of notebooks or JAR/whl/python scripts. It allows users to define recurring job schedules, dependencies, retries, and alerts. This feature is vital for production pipelines, such as daily ETL or model training, ensuring consistency and automation without manual intervention. 

5. How does Auto-scaling work in Databricks clusters? 

Answer: 
 Auto-scaling automatically adjusts the number of worker nodes in a cluster based on workload demands. It helps optimize performance and cost by scaling up during high activity and scaling down when usage decreases. This dynamic resource allocation is critical for large-scale, cost-effective Spark processing. 

6. What are the different types of Databricks clusters? 

Answer: 

  • Interactive clusters: Used for development and ad-hoc analytics in notebooks. 
  • Job clusters: Automatically created and terminated for scheduled jobs. 
  • SQL warehouses: Optimized for serving dashboards and BI queries. 

Each cluster type is designed for specific workloads, and Databricks training helps learners identify the best use cases for each. 

7. What is a Databricks Notebook and how is it used? 

Answer: 
 A Databricks Notebook is a web-based interface where users write and run code in various languages (Python, SQL, Scala, R). It supports visualizations, markdown, and rich outputs. Notebooks are ideal for exploratory data analysis, collaborative development, and sharing insights. 

8. How do you optimize Apache Spark jobs in Databricks? 

Answer: 
 Job optimization includes: 

  • Using caching to store interim results. 
  • Leveraging broadcast joins for small lookup tables. 
  • Monitoring Spark UI to diagnose skew and resource usage. 
  • Managing partitioning for better parallelism. 
  • Enabling adaptive query execution in newer Databricks runtimes. 

AccentFuture’s hands-on training covers real-world optimization scenarios using Databricks tools. 

9. What is MLflow and how is it integrated into Databricks? 

Answer: 
 MLflow is an open-source platform for managing the ML lifecycle, including experiment tracking, model packaging, and deployment. In Databricks, MLflow is natively integrated, making it easy to track experiments, register models, and deploy them into production using seamless UI and API-based workflows. 

 

10. What certifications are available for Databricks in 2025? 

Answer: 
 Databricks offers several certifications in 2025: 

  • Databricks Certified Data Engineer Associate 
  • Databricks Certified Data Engineer Professional 
  • Databricks Certified Machine Learning Associate 
  • Databricks Certified Lakehouse Fundamentals 

At AccentFuture, our Databricks certification courses prepare students with mock tests, case studies, and interview coaching to help them pass on the first attempt. 

Final Thoughts 

Preparing for Databricks interviews in 2025 requires a blend of theory, practical knowledge, and platform-specific experience. Whether you’re targeting roles in data engineering, analytics, or machine learning, mastering these questions will give you a competitive edge. 

Join AccentFuture’s Databricks online course to learn from industry experts, build hands-on projects, and gain confidence in tackling interviews with top companies. 

Want to know what to learn in Databricks before you apply? 

Keywords included: 

  • Databricks interview questions 
  • Databricks training online 
  • Databricks certification 
  • Delta Lake interview questions 
  • Spark optimization 
  • Best Databricks course 2025 
  • Apache Spark with Databricks 

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