Here’s a selection of Azure Databricks interview questions, split into categories to reflect the varying experience levels they might target:

Foundational Questions

• Define Azure Databricks and explain its core purpose.
• What are the key differences between Azure Databricks and plain Apache Spark?
• Describe the primary components within an Azure Databricks workspace.
• Explain the concept of a Databricks cluster. What are the different cluster types?
• What is Delta Lake? Highlight its advantages over storing data directly in a data lake.

Data Engineering Focus

• Outline a typical ETL process within Azure Databricks. What tools and techniques would you leverage?
• How would you handle slowly changing dimensions (SCDs) within a Databricks environment?
• Discuss performance optimization strategies for Spark jobs in Databricks.
• What are some common challenges with data quality in Databricks, and what are your best practices for management?
• How might you integrate Azure Data Factory with Azure Databricks for orchestration purposes?

Data Science & Machine Learning Focus

• Build a machine learning model in Databricks (data preparation, feature engineering, training, evaluation).
• What is MLflow, and how does it integrate with Azure Databricks?
• How would you approach hyperparameter tuning for a large-scale model in Databricks?
• Describe techniques for deploying and serving machine learning models created in Azure Databricks.
• How can you monitor the performance of production machine learning models within Databricks?

Advanced Topics

• Explain cluster autoscaling in Databricks. How do you configure it effectively?
• Discuss security considerations and best practices in an Azure Databricks environment (e.g., access controls, secrets management).
• How can you manage costs effectively when using Azure Databricks?
• Describe how you structure a Databricks project to encourage team collaboration and code reuse.
• What potential limitations or challenges to consider when working with Azure Databricks?

Tips for Interviewees

• Brush up on Spark fundamentals. Databricks builds on Spark so that interviewers will expect this knowledge.
• Understand concepts around distributed computing.
• Be ready to discuss specific projects. Explain the problem, your Databricks solution, and the outcomes.
• Highlight your ability to collaborate in shared workspaces.