30 Azure Databricks Interview Questions and Answers (2025)

Azure Databricks Overview

Azure Databricks is a unified open analytics platform built on Apache Spark with cloud-based accessibility. It offers a quick, user-friendly, and collaborative workspace for performing machine learning and big data processing tasks while providing AI solutions.

The platform is preferably used due to its 50 times faster performance, ability to run millions of server hours daily, easy navigation, effective security, and user productivity enhancement. Databricks finds applications in cloud infrastructure management, deployment, and security.

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Basic Azure Databricks Interview Questions for Beginners

1. What is Azure Databricks, and how does it integrate with Azure?

Azure Databricks is a data analytics and AI-based service offered by Microsoft Azure. It unifies data, the data ecosystem, and data teams. It is integrated with multiple Azure environments, such as Azure Data Lake Storage, Power BI, Azure Synapse Analytics, Azure Data Factory, and others, for advanced solutions and enhanced performance.

2. Can you explain the concept of a Databricks cluster and its components?

Databricks clusters refer to configurations and resources for running jobs and notebooks. There are two types of clusters: all-purpose and jobs. 

• The all-purpose cluster allows manual restart and termination. It can be shared for collaborative work. Creating this cluster requires REST API, CLI, and UI.
• The Databricks job scheduler can be used to create the job cluster. The latter terminates the cluster upon job completion and does not allow users to restart it.

3. What is Apache Spark, and how does Databricks utilize it?

Apache Spark is an open-source analytics engine that powers compute clusters and SQL warehouses. Azure Databricks offers a user-friendly, secure, and efficient platform for running Apache Spark workloads.

4. How do you create a workspace in Azure Databricks?

Workspace can be created in Azure Databricks through any of the following tools: Azure Portal, Azure CLI, PowerShell, ARM template, Bicep, and Terraform. To create a workspace in Azure Databricks, you can follow these steps:

• Step 1: Select Create a resource, followed by Analytics and Azure Databricks
• Step 2: Provide the values for creating a Databricks workspace
• Step 3: Choose the 'Review + Create' followed by 'Create'
• Step 4: Workspace will be created within a few minutes, regardless of deployment success or failure

If deployment succeeds, continue using it. If deployment fails, delete the workspace and create a new one without errors.

5. What are notebooks in Azure Databricks, and how do they help with data processing?

Notebooks are the primary tool for code development in different languages and for presenting results. They contribute to data processing by allowing team collaboration, automatic versioning, data analysis, environment customization, text writing in other languages, and built-in data visualizations.

Azure Databricks Interview Questions for Experienced

6. How do you scale a cluster in Azure Databricks, and what factors should you consider?

Scaling can be done in three ways: vertically by adding or removing resources, horizontally by editing the nodes of a distributed system, and linearly by adding resources to a system. Factors influencing scaling include the number of workers, cores, memory, local storage, complexity, data source, data partitioning method in external storage, and the need for parallelism. 

7. Can you explain how Delta Lake works in Azure Databricks?

Delta Lake helps store tables in Databricks by incorporating a transaction log on Parquet data files. It enables reliable ACID transactions and efficient and scalable metadata handling.

8. What is the process for migrating a Spark job from a local environment to Azure Databricks?

The process to migrate the Spark workload to Databricks involves the following steps:

• Change the parquet to delta
• Recompile Spark codes with Databricks Runtime compatible libraries
• Delete the SparkSession creation and terminal script commands

Now, you can run the workloads.

9. How do you troubleshoot performance issues in Azure Databricks?

Performance issues like partition skewing and executor misallocation can be handled using resource consumption metrics to identify the root cause and take appropriate corrective measures.

10. Explain the concept of Spark SQL and its usage in Databricks.

Spark SQL is a Spark module that enables structured data processing. It is used in Databricks to import relational data from Parquet files and Hive tables, among other functions.

Azure Databricks Scenario-Based Interview Questions

11. You are working on a large dataset, and the notebook takes too long to run. How would you optimize the performance in Azure Databricks?

To optimize notebook performance, analyze the Spark UI event log to assess the most time-consuming process. You can also increase the partition and driver size.

12. How would you handle a scenario where a Databricks cluster fails to start due to resource limitations?

The resource limitation can be addressed by freeing up resources by halting inactive clusters. It frees the CPU cores. Alternatively, you can request an increase in the account quota.

13. You must perform a real-time data analysis on a streaming dataset in Azure Databricks. How would you approach this?

Performing a real-time data analysis on a streaming dataset in Azure Databricks is possible using Apache Spark Structured Streaming. The approach will be:

• Connect to a Streaming Source:

Use sources like Apache Kafka, Azure Event Hubs, or socket streams. For Kafka:

df = spark.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "<broker>") \
.option("subscribe", "<topic>") \
.load()

• Parse and Transform the Data:

Convert the Kafka value to a readable format and apply transformations:

parsed_df = df.selectExpr("CAST(value AS STRING)")

• Apply Business Logic:

Use DataFrame transformations to filter, aggregate, or enrich data in real-time.

• Write the Output to a Sink:

Write to sinks such as Delta Lake, console, Azure Blob Storage, or SQL tables:

query = parsed_df.writeStream \
.format("delta") \
.outputMode("append") \
.option("checkpointLocation", "/mnt/checkpoints/") \
.start("/mnt/delta/output/")

• Monitor and Manage the Stream:

Use Databricks UI or Spark UI to monitor latency, throughput, and failures.

14. How would you ensure multiple users can access and modify the same notebook without conflict in a collaborative environment?

Access by multiple users creates multiple copies to prevent data loss. It results in an error message in the yellow information bar. To resolve the issue, you can perform the following steps:

• Use the stated bar in red to check the page displaying conflicting changes
• Copy the details from the error page and paste the same in the main page (if required)
• Now, right-click on the page tab and select 'Delete' on the shortcut menu to delete the conflicts page

15. A project requires integrating Azure Databricks with Azure Data Lake. Can you describe how you would set up this integration?

Databricks and Data Lake integration is possible in four ways:

• By using the service principal directly
• By using the Azure Data Lake Storage Gen2 storage account access key directly
• By transferring the Azure Data Lake Storage Gen2 filesystem to DBFS using a service principal and OAuth 2.0
• By credential passthrough

Azure Databricks Technical Interview Questions

16. How do you implement Spark streaming in Azure Databricks?

Data streaming with Spark Structured Streaming can be done by following a stepwise procedure that includes using a free API to read the information and transfer it into Azure Event Hub, configuring Databricks to read Event Hub, implementing a micro-batch process, and storing the data in a Delta table. Power BI will then read the data through direct query and process it for visualization. 

17. What is the difference between RDD and DataFrame in PySpark, and when should each be used in Azure Databricks?

PySpark RDD and PySpark DataFrame are both immutable distributed collections of data. However, RDD involves data partitioning across nodes, while a DataFrame has data organized into columns. RDD is preferred when a low-level transformation is needed on the dataset, but DataFrame is well-suited for structured data requiring SQL-like queries.

18. How would you handle data security in Azure Databricks for a multi-tenant environment?

Measures like authentication, access control, lockdown of outbound network access, encryption, secret management via authentication to external data sources, and auditing are among the measures to offer data security in Azure Databricks for a multi-tenant environment.

19. How can you automate the scheduling of jobs in Azure Databricks?

Automatic trigger of jobs in Azure Databricks is possible through the following steps:

• Open the job to be triggered, head to the 'Job Details' pane
• Scroll towards the 'Schedules & Triggers' section and click 'Add trigger'
• Select the type of trigger from scheduled, File arrival, or Continuous 
• Click 'Save'

If selecting File arrival, enter the path in Storage Location. You can also set and modify the minimum time difference between the triggers.

20. What are the advantages of using Apache Spark MLlib in Azure Databricks for machine learning?

The simple, secure, scalable, and easy-to-integrate feature of Spark's Machine Learning Library (MLLib) makes it a better option for usage in Databricks. Spark MLLib is pre-installed in the Databricks runtime and supports multiple programming languages such as Python, Scala, and Java.

Azure Databricks PySpark Interview Questions

21. What is PySpark, and how does it differ from Scala-based Spark?

PySpark is the Python API for Apache Spark. It allows large-scale data processing, performs real-time analysis, and offers a PySpark shell for data analysis. Considering the difference, Scala is concise and expressive, while Java is integrable and performs better. Python is more popular, easy to use, and comprises a rich data science ecosystem.

22. How do you perform data transformations in PySpark using Azure Databricks?

Data transformations include the development of a new DataFrame from an existing one. It can be done by using transformation methods like select(), groupBy(), sort(), join(), drop(), withColumn(), limit(), reparation(), distinct(), coalesce(), cast(), filter(), replace(), fillna(), replace() and dropna(). 

23. Can you explain how to read and write data from Azure Databricks to different storage systems using PySpark?

The process involves setting up the necessary Azure resources, such as a storage account and the Databricks workspace. In the Databricks notebook, PySpark allows interaction with different storage systems, like Azure Data Lake Storage (ADLS) or Azure Blob Storage.

Initially, the storage account can be mounted to the Databricks File System (DBFS) through Databricks utilities (dbutils.fs.mount). This simplifies the process of accessing and managing files kept in the cloud. After being mounted, data files like .csv can be accessed with spark.read.csv(). It can be saved in formats like Parquet, using DataFrame.write.parquet().

24. How do you optimize PySpark performance for large datasets in Databricks?

The PySpark performance optimization method for large datasets would include using multiple and adjusted smaller partitions, caching, memory management, data structure tuning, and using DataFrame/Dataset over Resilient Distributed Datasets (RDDs).

25. What is the purpose of groupBy and agg in PySpark, and how are they used in Databricks?

The groupBy() in PySpark forms a group of similar data, while the agg() executes different aggregations. The agg() can be said to perform operations on the grouped data. The groupBy() is used first to organize the records depending on the single or multiple column values, and agg() is used to gain the aggregate value in return.

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Azure Databricks Interview Questions for Data Engineers

26. How do you configure and manage Spark clusters in Azure Databricks for data engineering tasks?

The cluster configuration can be set to advanced options by selecting Compute>cluster>configuration>advanced options. It can also be done manually through a notebook or using the JOB CLI API. The stepwise approach to configuring and managing Databricks clusters includes:

• View the Databricks cluster list and 'Pin' the important ones among them
• Check for the Databricks cluster configured as JSON and export the same to have a copy
• Now, edit and clone the cluster
• You can also manage access via Cluster-creation permission and Cluster-level permission
• Terminate the unused clusters via the terminate option or enable Automatic Termination
• Delete the cluster always after termination, and restart it by clicking 'Restart' from the Kebab menu
• Cluster performance can be monitored by checking the details page for event logs and driver logs, which provide aggregated metrics of complete cluster activity; third-party tools can also be used
• Enable Spark decommissioning for effectively handling spot instance preemption by migrating shuffle and RDD data, reducing job failures, and data loss

27. What are some strategies for managing and processing large datasets in Azure Databricks?

Handling large datasets in Databricks requires strategies like managing the partitions according to the data, increasing the shuffle size and that of the driver to double the size of the executor, checking wide transformations, and ensuring that the data runs in a distributed manner.

28. How would you implement data pipelines in Azure Databricks for ETL processes?

Implementing data pipelines involves the following steps: 

• Create an ETL pipeline in DLT
• Use Databricks notebooks to develop and validate source code for DLT pipelines
• Query the processed data
• Create an automatic running job for data ingestion, processing, and analysis
• Schedule the job to run the ETL pipeline on schedule

29. What is your experience integrating Azure Databricks with Azure Data Factory for data engineering workflows?

Their integration first involves using Azure Data Factory for data movement and ELT & ETL processes. Azure Databricks provides the platform for advanced analytics, big data processing, and machine learning. The combinations allow end-to-end completion of data workflows with AI-based insights and advanced analytics.

30. Can you explain how to use Delta Lake for data versioning and auditing in Azure Databricks?

The time travel feature allows data versioning and auditing through Delta Lake. It allows querying and accessing data snapshots at specific points in time. Further, checking the transaction log assists in monitoring user activities and modifications.

Tips to Prepare for an Azure Databricks Interview

Preparing for the Azure Databricks interview requires focus and improvement in the following aspects:

• Gain hands-on experience with the Databricks platform
• Have familiarity with Databricks tutorials and documentation
• Review common Azure Databricks interview questions
• Stay current with the latest updates of the platform
• Be familiar with the integration of other Azure services
• Practice scenario-based questions in real-time

Conclusion

Organizations relying more on Microsoft Azure are on the constant lookout for qualified professionals. We have created these Databricks interview questions and answers to help assess and clarify the candidate's knowledge. They can also be leveraged for last-minute revision or to brush up on the information.

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