Azure Databricks Interview Questions

PART-1

 1. What is Azure Databricks?

• Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform provided as a fully managed service on Microsoft Azure.

2. What are the key components of Azure Databricks?

• Key components include the Databricks Workspace, Spark clusters, notebooks, libraries, jobs, and the Databricks Runtime.

3. What is Apache Spark, and why is it used in Azure Databricks?

• Apache Spark is an open-source, distributed computing system used for big data processing. It is used in Azure Databricks to process large volumes of data efficiently.

4. How does Azure Databricks differ from Databricks Community Edition?

• Azure Databricks is a fully managed, scalable, and secure version of Databricks that runs on Azure infrastructure. Databricks Community Edition is a free version with limited resources.

5. What is a Databricks workspace, and how is it used?

• A Databricks workspace is a collaborative environment for data engineering and data science tasks. It provides notebooks, libraries, and tools to work with data.

6. What are Databricks notebooks, and how are they useful?

• Databricks notebooks are interactive web-based environments for writing code, documenting analyses, and visualizing data. They are useful for collaborative data exploration and analysis.

7. What is Databricks Runtime, and why is it important?

• Databricks Runtime is a set of optimized Spark libraries and services provided by Databricks. It ensures compatibility and performance improvements for Spark clusters.

8. How do you create a Spark cluster in Azure Databricks?

• You can create a Spark cluster through the Azure Databricks portal by specifying the cluster configuration, including the instance type, number of nodes, and version.

9. What is a Databricks job, and how is it used?

• A Databricks job is a way to automate the execution of notebooks or JAR files on a scheduled or one-time basis. It is used for batch processing and ETL tasks.

10. How do you securely access data in Azure Databricks? – You can use Azure Blob Storage, Azure Data Lake Storage, and Azure Key Vault for secure data access. Databricks provides integration with Azure Active Directory for authentication.

11. What is Delta Lake, and how does it enhance data reliability in Azure Databricks? – Delta Lake is a storage layer that brings ACID transaction capabilities to Apache Spark and data reliability to Azure Databricks. It ensures data consistency and reliability.

12. What is the difference between a Delta table and a Parquet table in Azure Databricks? – Delta tables are ACID-compliant and provide transactional capabilities, while Parquet tables are not transactional. Delta tables are used when data reliability is critical.

13. How do you optimize the performance of Spark jobs in Azure Databricks? – You can optimize Spark jobs by using appropriate cluster configurations, tuning Spark settings, caching intermediate results, and optimizing data partitioning.

14. What is a Databricks library, and how is it used? – Databricks libraries are collections of code and dependencies that can be attached to a cluster. They are used to import external code, packages, and libraries into notebooks.

15. What are the advantages of using Databricks notebooks over traditional development environments? – Databricks notebooks offer real-time collaboration, interactive data exploration, version control, visualization capabilities, and seamless integration with Spark.

16. How do you integrate Azure Databricks with Azure Machine Learning services? – You can integrate Azure Databricks with Azure Machine Learning services by using the Azure Machine Learning SDK and the Databricks ML Runtime.

17. What is the role of MLflow in Azure Databricks, and how is it used? – MLflow is an open-source platform for managing machine learning lifecycles. In Azure Databricks, it helps with tracking, packaging, and deploying machine learning models.

18. What is the difference between Azure Databricks and Azure HDInsight? – Azure Databricks is a fully managed Apache Spark-based analytics platform, while Azure HDInsight is a cloud-based big data analytics platform that supports multiple open-source frameworks.

19. How can you monitor and optimize Azure Databricks workloads? – Azure Databricks provides monitoring and performance optimization through Databricks Runtime, cluster configuration, and integration with Azure Monitor.

20. What is the use of a Databricks job cluster in Azure Databricks? – A Databricks job cluster is a lightweight cluster used exclusively for running jobs. It helps isolate job execution from interactive cluster usage.

21. How do you handle data skew in Spark jobs running on Azure Databricks? – You can handle data skew by using techniques like data sampling, data shuffling, or by using techniques like bucketing or z-ordering for data distribution.

22. What is the role of Spark SQL in Azure Databricks, and how is it different from traditional SQL? – Spark SQL allows you to query structured data using SQL in Spark. It is different from traditional SQL as it can also handle unstructured and semi-structured data.

23. How do you secure data at rest and in transit in Azure Databricks? – Data at rest can be secured using encryption and Azure Storage encryption features. Data in transit can be secured using secure protocols and encryption.

24. How do you implement data lineage tracking in Azure Databricks? – You can implement data lineage tracking using tools like Apache Atlas or by integrating with external metadata stores.

25. What is the process for exporting Databricks notebooks as standalone Python or Scala applications? – You can export Databricks notebooks as standalone applications using the Databricks REST API or by converting them to Databricks libraries.

PART-2

1. What is Azure Databricks?

• Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative environment for data engineers, data scientists, and analysts to work together on big data and machine learning projects.

2. How does Azure Databricks differ from open-source Apache Spark?

• Azure Databricks provides a managed and fully integrated Spark platform with additional features such as automated cluster management, integration with Azure services, and collaborative workspace capabilities.

3. What is a Databricks workspace, and why is it important?

• A Databricks workspace is a collaborative environment that allows users to create and manage notebooks, jobs, clusters, and libraries. It is the central hub for data engineering and data science work in Databricks.

4. Explain the concept of a Databricks cluster.

• A Databricks cluster is a set of virtual machines (VMs) that are used to execute code in Databricks. Users can create and manage clusters based on their computing needs.

5. What is a Databricks notebook, and how is it used?

• A Databricks notebook is an interactive document that allows users to write and execute code, visualize results, and collaborate with others. It supports languages like Python, Scala, R, and SQL.

6. How do you create a Databricks notebook in the workspace?

• You can create a Databricks notebook by clicking on the “Create” button in the workspace and selecting “Notebook.” Then, you choose the notebook’s language and specify its location.

7. What is a Databricks job, and when would you use it?

• A Databricks job is an automated, scheduled execution of a notebook or a JAR file. You would use it to automate recurring tasks like data processing or model training.

8. How can you share notebooks and collaborate with others in Databricks?

• You can share notebooks with others by using workspace access control, sharing links, or collaborating within the notebook itself using Databricks collaborative features.

9. What are Databricks libraries, and why are they useful?

• Databricks libraries are additional packages and dependencies that can be attached to clusters or notebooks. They are useful for adding external libraries, such as machine learning frameworks, to your Databricks environment.

10. How do you install and manage libraries in Databricks? – You can install and manage libraries in Databricks using the “Libraries” tab in the cluster configuration or by specifying library requirements in a notebook.

11. What is Delta Lake, and how does it enhance data management in Databricks? – Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It enhances data management by providing data versioning, schema enforcement, and data consistency features.

12. How do you create a table in Delta Lake in Databricks? – You can create a table in Delta Lake by using the CREATE TABLE statement in SQL or by writing data to a specific path in Delta format.

13. What is Structured Streaming in Databricks, and how is it different from batch processing? – Structured Streaming is a real-time data processing API in Databricks that allows you to process data incrementally as it arrives, providing low-latency results. It differs from batch processing, which processes data in fixed-size chunks.

14. How do you handle data skew in Databricks? – Data skew can be mitigated in Databricks by using techniques such as partitioning, bucketing, and repartitioning to evenly distribute data across partitions and optimize query performance.

15. What is the Databricks MLflow project, and how does it help with machine learning workflow management? – MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It helps with tracking experiments, packaging code and dependencies, and deploying models.

16. What are Databricks clusters’ autoscaling capabilities, and how do they work? – Databricks clusters can automatically scale the number of worker nodes based on the workload. Autoscaling adjusts the cluster size up or down to match the processing needs, optimizing cost and performance.

17. How can you monitor and troubleshoot Databricks clusters’ performance? – Databricks provides cluster performance monitoring through the Databricks UI, logs, and integration with Azure Monitor. You can monitor cluster metrics and analyze logs to troubleshoot performance issues.

18. What is the difference between a Databricks Community Edition workspace and a Databricks Standard workspace? – The Community Edition is a free version of Databricks with limited resources, while the Standard workspace offers more features, scalability, and performance for enterprise use.

19. How can you secure data in Databricks? – Data in Databricks can be secured by configuring access controls, using managed identity or service principal authentication, and enabling encryption at rest and in transit.

20. How does Databricks integrate with Azure Active Directory (AAD) for user authentication? – Databricks can be configured to use Azure Active Directory for user authentication, allowing users to sign in with their Azure AD credentials and providing single sign-on (SSO) capabilities.

21. What is the purpose of the Databricks File System (DBFS)? – DBFS is a distributed file system in Databricks used for storing and managing data files. It provides a unified file system view for accessing data stored in various locations.

22. Explain the concept of Databricks Jobs REST API and how it can be used. – The Databricks Jobs REST API allows you to programmatically create, manage, and trigger jobs. It can be used to automate job execution and integrate Databricks with other systems.

23. What is the difference between a Databricks Standard cluster and a Databricks High Concurrency cluster? – A Databricks High Concurrency cluster is optimized for multiple users running interactive queries and notebooks concurrently, while a Standard cluster is designed for data engineering and batch processing workloads.

24. How do you optimize Spark SQL queries for better performance in Databricks? – You can optimize Spark SQL queries in Databricks by using techniques like query optimization, indexing, caching, and partition pruning.

25. What is the purpose of the Databricks Runtime? – Databricks Runtime is an integrated runtime environment for running Apache Spark workloads. It includes optimized versions of Spark and other components, as well as Databricks-specific enhancements.

26. How do you handle missing or incomplete data in Databricks? – You can handle missing or incomplete data in Databricks by using techniques such as data imputation, filtering, or aggregation based on your specific use case.

27. What is the Databricks CLI, and how can it be used? – The Databricks Command Line Interface (CLI) is a tool for managing Databricks resources and clusters from the command line. It can be used for automation and scripting tasks.

28. How does Databricks integrate with Azure Data Lake Storage (ADLS)? – Databricks can integrate with Azure Data Lake Storage (ADLS) for storing and processing data. You can mount ADLS as a file system in Databricks and access data seamlessly.

29. Explain the concept of a Databricks interactive cluster. – An interactive cluster in Databricks is designed for interactive data analysis, exploration, and visualization. It provides a responsive environment for running notebooks and queries.

30. What are the advantages of using Azure Databricks for machine learning tasks? – Azure Databricks simplifies the end-to-end machine learning process, provides a collaborative workspace, and integrates with Azure Machine Learning services for model deployment and management.

PART-3

1. What is Azure Databricks?

• Azure Databricks is an Apache Spark-based analytics platform provided as a fully managed service on Microsoft Azure. It is designed for big data analytics and data engineering tasks.

2. How is Azure Databricks different from Apache Spark?

• Azure Databricks is a managed Spark platform that simplifies deployment, management, and scaling. It offers integration with Azure services, collaborative features, and improved security.

3. What are the key components of Azure Databricks?

• Key components include the Azure Databricks Workspace, clusters, notebooks, libraries, and job scheduler.

4. What is a Databricks cluster, and how is it managed in Azure Databricks?

• A Databricks cluster is a set of virtual machines used for running Spark jobs. In Azure Databricks, clusters can be created, configured, and managed through the Azure portal or Databricks workspace.

5. How is data storage managed in Azure Databricks?

• Azure Databricks can use Azure Data Lake Storage or other Azure storage solutions for data storage. Data can be read from and written to these storage services using Databricks File System (DBFS) or other supported data sources.

6. Explain the role of notebooks in Azure Databricks.

• Notebooks in Azure Databricks are interactive, web-based environments for writing, executing, and documenting code. They are commonly used for data exploration, analysis, and collaborative work.

7. What is the Databricks Runtime in Azure Databricks?

• Databricks Runtime is a versioned, managed runtime environment for running Spark jobs in Azure Databricks. It includes Apache Spark, libraries, and optimizations.

8. How can you create a new notebook in Azure Databricks?

• You can create a new notebook in Azure Databricks by navigating to the Databricks workspace, selecting a folder, and clicking the “Create” button to create a new notebook. You can choose the default language (e.g., Scala, Python) for the notebook.

9. What is a library in Azure Databricks, and how can you install one?

• A library in Azure Databricks is a collection of code and resources that can be used in notebooks. You can install libraries by navigating to the “Libraries” tab in the workspace and clicking the “Install New” button to specify the library source.

10. How can you share notebooks with other users in Azure Databricks? – You can share notebooks in Azure Databricks by setting the access control list (ACL) to allow specific users or groups to view or edit the notebook. You can also create a shared folder and manage permissions for notebooks within that folder.

11. What is the purpose of a Databricks job in Azure Databricks? – A Databricks job in Azure Databricks is used to schedule the execution of a notebook or JAR file. It allows you to automate and schedule data processing tasks.

12. How can you schedule a Databricks job in Azure Databricks? – You can schedule a Databricks job by specifying the notebook, libraries, and cluster configuration. You can set the frequency and parameters for job execution using the job scheduler.

13. Explain the integration of Azure Databricks with Azure Machine Learning. – Azure Databricks can be integrated with Azure Machine Learning to streamline machine learning workflows. You can use Azure Databricks for data preparation, training models, and deploying models to Azure Machine Learning services.

14. What is Delta Lake, and how does it improve data management in Azure Databricks? – Delta Lake is an open-source storage layer that brings ACID transactions and versioning to data lakes. It provides data quality, reliability, and performance improvements when working with data in Azure Databricks.

15. How does Azure Databricks handle data security and authentication? – Azure Databricks integrates with Azure Active Directory (Azure AD) for authentication and role-based access control (RBAC). It also supports encryption at rest and in transit for data security.

16. What are some common use cases for Azure Databricks in data analytics and engineering? – Common use cases include data exploration, ETL (Extract, Transform, Load) processes, data preparation for machine learning, real-time stream processing, and data warehousing.

17. How does Azure Databricks handle job retries and error handling? – Azure Databricks provides job retries and error handling through job configurations. You can specify the number of retries and the behavior on failure for a job.

18. What is the difference between Databricks Community Edition and the paid Azure Databricks service? – Databricks Community Edition is a free version of Databricks with limited resources and capabilities. The paid Azure Databricks service provides enhanced features, scalability, and integration with Azure services.

19. Explain the benefits of using Azure Databricks with Azure Synapse Analytics (formerly SQL Data Warehouse). – Azure Databricks and Azure Synapse Analytics can be integrated to combine the power of data engineering and data warehousing, enabling efficient data processing and analytics at scale.

20. How can you optimize the performance of Spark jobs in Azure Databricks? – Performance optimization techniques include cluster sizing, data partitioning, caching, and using appropriate data formats. Azure Databricks provides performance monitoring and tuning capabilities.

21. What is the purpose of Spark MLlib in Azure Databricks, and how can it be used for machine learning? – Spark MLlib is a machine learning library in Spark. In Azure Databricks, it can be used for building and training machine learning models, making it easier to leverage big data for predictive analytics.

22. How can you monitor the resource utilization and performance of Azure Databricks clusters? – Azure Databricks provides cluster monitoring and debugging tools, including cluster logs, performance metrics, and the ability to scale clusters based on workload requirements.

23. Explain the process of importing data into Azure Databricks from external sources such as Azure Blob Storage or Azure SQL Database. – Data can be imported into Azure Databricks using various connectors and libraries. You can specify the source and destination, configure access credentials, and use Spark APIs or notebooks to read and write data.

24. What is the benefit of using the Delta Lake format in Azure Databricks for data storage? – Delta Lake provides ACID transactions and versioning, making it suitable for building reliable data pipelines and ensuring data consistency and quality.

25. How can you handle schema evolution and data versioning in Azure Databricks using Delta Lake? – Delta Lake allows you to evolve schemas and handle data versioning using features such as schema evolution and time travel.

26. What is the purpose of the Databricks REST API, and how can it be used in Azure Databricks? – The Databricks REST API allows programmatic access to Azure Databricks resources and operations. It can be used for automating tasks, job scheduling, and managing clusters.

27. Explain the concept of Spark Streaming in Azure Databricks and its use cases. – Spark Streaming is used for processing real-time data streams. In Azure Databricks, it can be used for applications like fraud detection, log analysis, and monitoring.

28. How does Azure Databricks handle data lineage and auditing for data governance and compliance? – Azure Databricks provides data lineage tracking and auditing capabilities, allowing organizations to trace data from source to destination and meet compliance requirements.

29. What is the significance of the Databricks Community in Azure Databricks? – The Databricks Community provides a platform for collaboration, sharing of best practices, and access to community-contributed notebooks and libraries.

30. Explain the use of widgets in Azure Databricks notebooks. – Widgets are interactive controls that can be added to notebooks. They allow users to input values, parameters, or options interactively, making notebooks more flexible and user-friendly.

PART-4 : Scenario based

1. Scenario: You are tasked with processing large volumes of log data from multiple sources in real-time. How would you use Azure Databricks for this real-time stream processing?

Answer:

• Create a Databricks cluster optimized for streaming workloads.
• Use Spark Streaming or Structured Streaming in Databricks to ingest, process, and analyze real-time log data from sources like Azure Event Hubs or Kafka.
• Implement transformations and aggregations to gain insights from the data.

2. Scenario: Your organization wants to perform anomaly detection on IoT sensor data stored in Azure Blob Storage. How can you leverage Azure Databricks for this task?

Answer:

• Create a Databricks notebook.
• Configure access to Azure Blob Storage using a shared access signature (SAS) or managed identity.
• Load the IoT sensor data into a Databricks DataFrame.
• Use machine learning algorithms or Spark MLlib in Databricks to build models for anomaly detection.

3. Scenario: You need to join and analyze data from multiple sources, including Azure SQL Database, Azure Cosmos DB, and Azure Data Lake Storage, using Azure Databricks. How would you approach this data integration task?

Answer:

• Create linked services for Azure SQL Database, Azure Cosmos DB, and Azure Data Lake Storage.
• Use Databricks notebooks to load data from these sources into DataFrames.
• Perform data transformations, joins, and aggregations as needed.
• Store the results in a suitable destination, such as Azure Data Lake Storage or Azure Synapse Analytics.

4. Scenario: Your organization wants to build a recommendation engine for an e-commerce website using collaborative filtering. How can you use Azure Databricks for this machine learning task?

Answer:

• Load user-item interaction data into Databricks DataFrames.
• Use the Spark MLlib library in Databricks to train collaborative filtering models.
• Deploy the trained models for recommendations in a production environment, such as Azure SQL Database or a web service.

5. Scenario: You are responsible for managing a team of data engineers working with Azure Databricks. How can you ensure that multiple team members can collaborate efficiently on Databricks notebooks?

Answer:

• Set up a Databricks Workspace and create a shared folder for team collaboration.
• Use access control lists (ACLs) to manage permissions for notebooks within the shared folder.
• Encourage team members to use version control systems like Git for collaboration and code management.

6. Scenario: Your organization needs to perform daily ETL (Extract, Transform, Load) tasks on data stored in Azure Blob Storage. How can you schedule these data pipelines in Azure Databricks?

Answer:

• Create Databricks notebooks for ETL tasks.
• Schedule the notebooks to run as jobs using the Databricks job scheduler.
• Configure job parameters to specify the data source, destination, and other parameters.

7. Scenario: You want to build a data pipeline that ingests data from various sources, processes it, and stores the results in an Azure Synapse Analytics (formerly SQL Data Warehouse) database. How can you design this pipeline using Azure Databricks?

Answer:

• Create linked services for data sources, Azure Synapse Analytics, and Azure Data Lake Storage.
• Use Databricks notebooks to load data from sources, perform transformations, and write the results to Azure Synapse Analytics.

8. Scenario: Your organization is migrating from on-premises data infrastructure to Azure, and you need to move and transform large volumes of data to Azure Data Lake Storage. How can you achieve this using Azure Databricks?

Answer:

• Set up a Databricks cluster with sufficient resources.
• Create linked services for the on-premises data source and Azure Data Lake Storage.
• Use Databricks notebooks to orchestrate the data movement and transformations.
• Schedule the notebooks to run at specified intervals to keep data up to date.

9. Scenario: You want to build a data pipeline that processes customer feedback text data using natural language processing (NLP) techniques. How can you use Azure Databricks for this task?

Answer:

• Load customer feedback data into a Databricks DataFrame.
• Use NLP libraries and techniques within Databricks notebooks to perform sentiment analysis, entity recognition, or topic modeling.
• Visualize the results or store them in a suitable format for reporting.

10. Scenario: Your organization wants to monitor and visualize real-time data metrics from a fleet of IoT devices. How can you implement a real-time dashboard using Azure Databricks?

Answer:

• Ingest data from IoT devices using a streaming source like Azure Event Hubs or Kafka.
• Use Databricks notebooks to process and aggregate the data in real-time.
• Create interactive visualizations using libraries like Matplotlib or display the results in real-time dashboards using tools like Databricks Delta and Power BI.

11. Scenario: You need to perform automated regression testing on Spark jobs running in Azure Databricks. How can you set up and automate this testing process?

Answer:

• Create a separate Databricks cluster for regression testing.
• Develop test scripts or notebooks that validate the expected outcomes of Spark jobs.
• Schedule the test notebooks to run automatically on the regression testing cluster.
• Use assertions and comparisons to check the results against expected values and notify the team in case of failures.

12. Scenario: You want to build a recommendation engine for a video streaming platform using collaborative filtering and deploy it as an API for real-time recommendations. How can you achieve this using Azure Databricks?

Answer:

• Train collaborative filtering models in Databricks using historical user-interaction data.
• Deploy the trained models as a REST API using Azure Databricks’ REST endpoints or a serverless Azure Function.
• Integrate the API with the video streaming platform to provide real-time recommendations to users.

13. Scenario: You need to optimize the performance of a slow-running Spark job in Azure Databricks. How can you identify bottlenecks and improve job execution time?

Answer:

• Use Databricks’ monitoring and profiling tools to identify bottlenecks, such as slow data reads or transformations.
• Optimize data partitioning and caching.
• Consider scaling the Databricks cluster or choosing a more suitable instance type.
• Profile and refactor code to remove performance bottlenecks.

14. Scenario: Your organization wants to implement a data governance strategy for Azure Databricks. How can you enforce data lineage, auditing, and access control?

Answer:

• Configure Azure Databricks to use Azure AD for authentication and RBAC for access control.
• Enable data lineage tracking and auditing in Azure Databricks settings.
• Use folder structures and access control lists (ACLs) to manage permissions and organize notebooks.
• Implement version control for notebooks using Git or Databricks’ built-in versioning.

15. Scenario: You are building a data lake architecture using Azure Databricks and need to ensure data consistency and reliability. How can you use Delta Lake to achieve this?

Answer:

• Use Delta Lake as the storage layer for your data lake.
• Leverage Delta Lake’s ACID transactions for data consistency.
• Enable schema evolution and versioning to handle changes to data structures.
• Implement data quality checks and validations using Delta Lake’s features.

16. Scenario: Your organization wants to automate the provisioning and scaling of Azure Databricks clusters based on workload demands. How can you achieve auto-scaling in Azure Databricks?

Answer:

• Configure auto-scaling settings in Azure Databricks to define scaling policies based on metrics like CPU usage, memory usage, or pending tasks.
• Specify the minimum and maximum number of worker nodes for the cluster.
• Azure Databricks will automatically adjust the cluster size according to the defined policies.

17. Scenario: You want to use Azure Databricks for data preparation and feature engineering for a machine learning project. How can you integrate Databricks with Azure Machine Learning for model training and deployment?

Answer:

• Use Databricks notebooks to prepare and engineer features from raw data.
• Train machine learning models using libraries like Scikit-Learn or Spark MLlib in Databricks.
• Deploy the trained models as a web service using Azure Machine Learning.
• Integrate the deployed web service with your application for real-time predictions.

18. Scenario: Your organization is migrating data and workloads from an on-premises Hadoop cluster to Azure Databricks. How can you plan and execute this migration efficiently?

Answer:

• Assess the existing Hadoop workloads and data dependencies.
• Create Azure Databricks clusters with appropriate configurations.
• Use Azure Data Factory or other ETL tools to migrate data from Hadoop HDFS to Azure Data Lake Storage or other suitable Azure data stores.
• Refactor and adapt Hadoop jobs to run on Azure Databricks.
• Test and validate the migration process.

19. Scenario: You are responsible for monitoring and optimizing costs in Azure Databricks. How can you control and reduce costs while ensuring efficient resource utilization?

Answer:

• Monitor and analyze cluster utilization metrics to rightsize clusters.
• Implement auto-scaling to adjust cluster sizes based on workload demands.
• Use Azure Cost Management and Billing to track Databricks-related costs and set budget alerts.
• Encourage cost-conscious practices such as pausing clusters during idle periods.

20. Scenario: Your organization wants to implement a disaster recovery (DR) strategy for Azure Databricks to ensure business continuity. How can you design and implement a DR plan for Databricks?

Answer:

• Set up a backup workspace in a secondary Azure region.
• Periodically replicate Databricks notebooks, libraries, and job configurations to the secondary workspace.
• Implement cross-region data replication for critical data stores such as Azure Data Lake Storage or Delta Lake tables.
• Document the DR plan and test it regularly to ensure readiness.