Azure Databricks Interview Questions


  Azure Databricks Interview Questions

  • 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.

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