Databricks Interview Questions and Answers for 2 years experience

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Databricks Interview Questions & Answers (2 Years Experience)

  1. What is Databricks?

    • Answer: Databricks is a unified analytics platform built on Apache Spark. It combines data engineering, data warehousing, machine learning, and data science capabilities in a single, collaborative environment. It simplifies the process of working with big data by providing managed infrastructure, optimized performance, and collaborative tools.

  2. Explain the architecture of Databricks.

    • Answer: Databricks' architecture is based on a cluster manager, which allocates and manages resources (compute, memory, storage). It utilizes Spark for distributed computation. Data is stored in various data lakes (like AWS S3, Azure Blob Storage, or Google Cloud Storage), and Databricks provides access and processing capabilities. It includes a collaborative workspace for users to interact with notebooks, code, and data.

  3. What are the different cluster modes in Databricks?

    • Answer: Databricks offers several cluster modes: Standard, High Concurrency, and All Purpose. Standard clusters are best for batch processing and long-running jobs. High Concurrency clusters are optimized for interactive workloads and many short-lived jobs. All Purpose clusters aim to balance both batch and interactive workloads.

  4. How do you optimize Spark performance in Databricks?

    • Answer: Optimizing Spark performance in Databricks involves several strategies: choosing the right cluster type and size, tuning Spark configurations (e.g., `spark.sql.shuffle.partitions`, `spark.executor.memory`), using optimized data formats (Parquet, ORC), partitioning data effectively, caching frequently accessed data, and using vectorized operations.

  5. Explain the concept of Delta Lake in Databricks.

    • Answer: Delta Lake is an open-source storage layer that provides ACID transactions, schema enforcement, and data versioning on top of data lakes (like S3, Azure Blob Storage). It enhances data reliability and improves data governance in Databricks.

  6. What are Databricks notebooks? How are they used?

    • Answer: Databricks notebooks are interactive coding environments where users can write, execute, and share code, visualizations, and results. They support various languages (like Scala, Python, R, SQL) and integrate seamlessly with the Databricks platform for data analysis and machine learning tasks.

  7. How do you handle errors and exceptions in Databricks jobs?

    • Answer: Error handling involves using `try-except` blocks (Python) or `try-catch` blocks (Scala) to gracefully handle exceptions. Logging errors is crucial for debugging and monitoring. Databricks also provides monitoring tools to track job execution and identify issues.

  8. Describe your experience with Databricks SQL.

    • Answer: [This answer should be personalized based on the candidate's experience. It should detail their usage of Databricks SQL, including query writing, performance optimization, data exploration, and any specific projects or tasks completed using Databricks SQL.]

  9. Explain your experience with data transformation in Databricks.

    • Answer: [This answer should be personalized and describe the candidate's experience with data transformation using PySpark or Spark SQL. It should include examples of data cleaning, manipulation, aggregation, and other transformation techniques.]

  10. How do you manage access control and security in Databricks?

    • Answer: Databricks offers granular access control through its user management system. Roles and permissions can be assigned to users and groups, limiting access to specific data, clusters, and notebooks. Integration with existing identity providers (like Azure Active Directory or Okta) is also possible for centralized identity management.

  11. Explain your experience with Auto Loader in Databricks.

    • Answer: [Personalized answer detailing experience with Auto Loader, including its use for efficient data ingestion from various sources and its benefits over manual ingestion methods.]

  12. How do you monitor and troubleshoot Databricks jobs?

    • Answer: [Explain the use of Databricks monitoring tools, logs, and metrics for troubleshooting. Include specific examples from past experiences.]

  13. What are the different ways to deploy machine learning models built in Databricks?

    • Answer: [Discuss various deployment options like model registry, MLflow, and integrating with other applications.]

  14. How do you handle large datasets in Databricks?

    • Answer: [Explain techniques like data partitioning, caching, and using optimized data formats to process large datasets efficiently.]

  15. Explain your experience with Databricks Unity Catalog.

    • Answer: [If familiar, explain the role of Unity Catalog in data governance and security.]

  16. What are some best practices for writing efficient Spark code?

    • Answer: [Discuss best practices like avoiding unnecessary shuffles, using broadcast variables effectively, and optimizing data structures.]

  17. Describe your experience with scheduling jobs in Databricks.

    • Answer: [Describe experience with scheduling tools and techniques, including setting up recurring jobs and managing dependencies.]

  18. How do you handle data versioning in Databricks?

    • Answer: [Explain the use of Delta Lake, Git integration, or other version control methods.]

  19. Explain your understanding of Spark's different execution plans.

    • Answer: [Discuss different execution plans and how they impact performance.]

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