Databricks Interview Questions and Answers for experienced

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100 Databricks Interview Questions and Answers

  1. What is Databricks and what are its key components?

    • Answer: Databricks is a unified analytics platform built on Apache Spark. Its key components include the Databricks Unified Analytics Platform, offering collaborative workspaces, managed Spark clusters, and various integrated services; the Databricks Runtime (DBR), providing optimized Spark environments; and Databricks SQL, a serverless SQL warehouse.

  2. Explain the architecture of a Databricks cluster.

    • Answer: A Databricks cluster comprises a driver node (master node) and multiple worker nodes. The driver node manages the execution of the application, while worker nodes perform the actual computation. These nodes can be provisioned in various cluster types (all-purpose, compute-optimized, memory-optimized), providing flexibility based on workload demands. Databricks handles the underlying infrastructure management.

  3. Describe the different cluster modes in Databricks.

    • Answer: Databricks offers several cluster modes: Standard clusters (always-on), which remain active until manually terminated; Autoscaling clusters, which automatically adjust the number of worker nodes based on workload; and Serverless clusters, which automatically provision resources as needed and scale down to zero when idle. Each mode offers trade-offs between cost and performance.

  4. How do you optimize Spark jobs in Databricks?

    • Answer: Optimizing Spark jobs involves various strategies: proper data partitioning and data format selection (Parquet is generally preferred); using broadcast variables for small datasets; leveraging caching effectively; tuning Spark configurations (e.g., `spark.executor.cores`, `spark.executor.memory`); employing techniques like data skew mitigation, and using optimized execution plans.

  5. What are Delta Lake tables and their advantages over traditional formats like Parquet?

    • Answer: Delta Lake is an open-source storage layer that provides ACID transactions, schema enforcement, and time travel capabilities on top of data lakes. Compared to Parquet, it offers enhanced data reliability, data versioning, and improved data quality through schema evolution and data integrity features.

  6. Explain how to handle data security in Databricks.

    • Answer: Databricks offers various security features: Access Control Lists (ACLs) for managing user permissions; encryption at rest and in transit; integration with cloud-based identity providers (like Azure Active Directory or AWS IAM); network security configurations (e.g., VPC peering); and data masking or anonymization techniques.

  7. How do you monitor and troubleshoot Spark jobs in Databricks?

    • Answer: Databricks provides a robust monitoring system: the Databricks UI offers real-time job monitoring; the Spark UI provides detailed insights into job stages and execution; logs can be analyzed to identify bottlenecks; and Databricks offers integrations with monitoring tools like Datadog or CloudWatch for centralized monitoring and alerting.

  8. What are some common performance bottlenecks in Databricks and how to address them?

    • Answer: Common bottlenecks include insufficient resources (memory, cores), inefficient data partitioning, data skew, slow I/O operations, and network latency. Addressing these requires careful cluster sizing, optimized data structures, data skew mitigation techniques, using appropriate data formats, and ensuring efficient network connectivity.

  9. Describe your experience with Databricks SQL.

    • Answer: [Provide a detailed answer based on your experience, mentioning features used, query optimization techniques applied, and any challenges encountered and how they were overcome. Include examples of queries or use cases].

  10. Explain your experience with Databricks notebooks.

    • Answer: [Describe your experience using Databricks notebooks, including code organization, version control practices (e.g., Git integration), collaboration features, and how you utilize notebooks for data exploration, analysis, and reporting. Include specific examples of notebook use cases].

  • How do you manage dependencies in Databricks?

    • Answer: Dependencies are managed using Maven or PyPI repositories. We define dependencies in `pom.xml` (for Maven) or `requirements.txt` (for Python) files, and Databricks automatically handles the download and installation of necessary libraries during cluster creation.

  • Explain your experience with Auto Loader in Databricks.

    • Answer: [Describe experience with Auto Loader, including its use for streaming data ingestion, handling various data formats, and managing schema evolution. Include any performance optimization techniques used].

  • How do you integrate Databricks with other cloud services?

    • Answer: Databricks integrates well with various cloud services. For example, we can connect to cloud storage (AWS S3, Azure Blob Storage, GCP Cloud Storage), databases (e.g., Snowflake, Redshift), and other cloud-based tools using connectors and APIs. We've leveraged these integrations for data ingestion, processing, and reporting.

  • Describe your experience using MLflow in Databricks.

    • Answer: [Detail your experience with MLflow for managing the ML lifecycle: experiment tracking, model versioning, model deployment, and model registry. Include examples of models you've trained and deployed using MLflow in Databricks].

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