Here’s a comprehensive guide to Databricks interview questions, categorized by category and experience level.

Key Concepts & Architecture

• What is Databricks?  Explain its primary purpose and how it sits within the extensive data landscape.
• Describe the core components of the Databricks architecture. (Workspaces, clusters, notebooks, jobs, Databricks File System (DBFS), etc.)
• What is a Databricks cluster? Differentiate between standard, job, and all-purpose clusters.
• Explain Delta Lake. What advantages does it bring over traditional data lake formats?
• How do you implement Databricks clusters for cost optimization? (Cluster sizing, auto-scaling, spot instances)

Spark Fundamentals

• What are RDDs? How do they underpin distributed computing in Spark?
• Explain the difference between transformations and actions in Spark.
• Describe common Spark transformations (map, filter, reduceByKey, join, etc.) and provide use cases.
• Describe everyday Spark actions (collect, count, take, foreach, etc.) and when to use them.
• How do you optimize Spark jobs? Discuss techniques like partitioning, caching, and avoiding shuffles.

Data Engineering

• How do you ingest data from various sources (databases, cloud storage, streaming) into Databricks?
• Describe a typical ETL process within Databricks. What tools and operations do you use?
• Explain the data quality checks you implement in Databricks pipelines.
• How do you schedule and orchestrate Databricks jobs?
• Discuss strategies for monitoring Databricks jobs and troubleshooting issues.

Data Analysis & ML

• What are the different libraries available for data exploration and visualization within Databricks?
• How do you use Databricks notebooks for exploratory data analysis (EDA)?
• Describe the process of feature engineering in Databricks.
• Explain MLflow. How do you use it for model tracking and deployment?
• How do you perform hyperparameter tuning in a Databricks environment?

Experience-Based Questions

Fresher/Junior

• Describe a data-related project you’ve worked on. How did you use Spark or similar tools?
• Given a scenario, how would you design a primary ETL pipeline in Databricks?
• Explain the different file formats supported in Databricks (CSV, Parquet, JSON, etc.) and when to use each one.

Experienced

• Discuss performance optimization challenges you’ve faced in large-scale Databricks implementations and how you addressed them.
• How have you handled data security and governance in Databricks, especially in sensitive environments?
• Describe how you collaborate with other teams (data scientists, analysts) on Databricks projects.
• How do you integrate Databricks with other cloud services (e.g., Azure Blob Storage, AWS S3)?

Tips for Answering Databricks Interview Questions

• Show real-world understanding: Don’t just memorize definitions; demonstrate how you’ve applied Databricks concepts in projects.
• Tailor your answers:  Focus on the skills relevant to the job description and the company’s specific use cases.
• Explain your thought process: Walk through your reasoning behind design choices and problem-solving approaches.
• Be prepared to code: Some interviews may involve live coding challenges or whiteboarding exercises.