Need help preparing for your Azure Databricks interview? With more companies adopting cloud platforms – the demand for Azure Databricks skills is growing rapidly. Did you know? In 2024, Databricks reported over $1.6 billion in revenue, representing more than 50% year-over-year growth. This growth highlights the increasing importance of Azure Databricks in the data and AI industry. If you are aiming to land a job in this field, it is important to be ready for common interview questions. In this blog, we’ll cover the top 35+ Azure Databricks interview questions and answers to help you feel confident and prepared for your interview.
Fun Fact: Over 10,000 companies around the world use Azure Databricks, including big names like Comcast, Rivian, and Block.
Basic Azure Databricks Interview Questions for Beginners
Here are some basic Azure Databricks interview questions and answers.
- What is Azure Databricks?
Azure Databricks is a cloud-based analytics platform. It is built on Apache Spark and designed for big data and AI workloads. It helps data engineers and scientists process and analyse large datasets easily.
- What are the key components of Azure Databricks?
Azure Databricks has three main components:
- Workspace: For managing projects and organising notebooks.
- Clusters: For running and processing data.
- Jobs: For automating and scheduling tasks.
- How is Azure Databricks integrated with Azure services?
Azure Databricks seamlessly integrates with Azure services. These include Azure Data Lake, Azure SQL Database, and Azure Synapse Analytics. It also connects with Azure Active Directory for security and access control.
- What programming languages does Azure Databricks support?
Azure Databricks supports multiple languages. These include Python, R, Scala, Java, and SQL. This flexibility makes it suitable for various data tasks.
- What are the benefits of using Azure Databricks?
Azure Databricks offers scalability, fast processing, and real-time data insights. It integrates with Azure services, supports collaborative workspaces, and reduces development time.
Also Read - Top 10 Most Popular Programming Languages of the Future
Azure Databricks Interview Questions for Freshers
Now, let’s take a look at some commonly asked Azure Data Bricks interview questions and answers for freshers.
- How does Azure Databricks simplify big data processing?
Azure Databricks automates cluster management and optimises Apache Spark. It enables fast processing of big data. Its user-friendly interface makes it easier to work with data at scale.
- What is the purpose of a notebook in Azure Databricks?
A notebook is a web-based interface in Azure Databricks. It allows users to write and execute code, visualise data, and share results. Notebooks support multiple languages like Python, SQL, and Scala.
- What is a Databricks cluster?
A Databricks cluster is a set of virtual machines. It is used to run big data and AI tasks. Clusters can be scaled up or down based on workload requirements.
- What are Databricks Workspaces used for?
Workspaces in Azure Databricks help users organise their work. They store notebooks, libraries, and dashboards in a structured manner. This allows easy collaboration and management.
- What is the role of Apache Spark in Azure Databricks?
Apache Spark is the core engine behind Azure Databricks. It powers data processing, machine learning, and streaming tasks. Databricks enhances Spark by providing a simplified interface and better performance.
Azure Databricks Interview Questions for Experienced
Here are some important Azure Databricks interview questions and answers for experienced candidates.
- How does Azure Databricks handle large-scale data?
Azure Databricks uses distributed computing with Apache Spark. It processes large-scale data by dividing tasks into smaller parts. These tasks run parallelly across clusters for faster processing.
- What is the role of Delta Lake in Azure Databricks?
Delta Lake is a storage layer in Azure Databricks. It ensures data reliability with features like ACID transactions and version control. It also improves performance by enabling efficient querying and updates.
- How can you optimise performance in Azure Databricks?
Performance can be optimised by:
- Using Auto-Scaling Clusters to match workload demands.
- Caching frequently used data.
- Writing optimised queries and partitioning large datasets.
- What is the difference between Azure Databricks and Azure Synapse Analytics?
Azure Databricks is designed for big data analytics and AI workloads. Azure Synapse Analytics focuses on data integration and warehousing. Databricks uses Apache Spark, while Synapse supports SQL-based queries and ETL pipelines.
- What is the significance of Databricks Runtime?
Databricks Runtime is a pre-configured environment. It includes optimised libraries for machine learning, data analytics, and processing. Different runtime versions offer specific enhancements for various tasks.
Azure Databricks Scenario Based Interview Questions
These are some important Databricks scenario based interview questions and answers.
- How would you troubleshoot a failed job in Azure Databricks?
“If a job fails, I start by checking the job logs to understand the root cause. I look for error messages or stack traces to pinpoint the issue. Next, I review the cluster’s configuration to ensure it has the necessary resources. If the failure is due to missing libraries, I install them and rerun the job. I also verify the script parameters to ensure there are no mistakes.”
- A cluster is running slowly. How do you resolve this?
“When a cluster runs slowly, I begin by reviewing the performance metrics, such as CPU and memory usage. If the cluster is under-resourced, I scale it up or enable auto-scaling to match the workload. I also check for bottlenecks in the code, such as inefficient queries or non-optimised Spark operations. Adjusting Spark configurations, like increasing executor memory or parallelism, is another step I take to improve performance.”
- How would you implement a real-time streaming pipeline in Azure Databricks?
“I would use Spark Structured Streaming in Databricks. First, I connect to a data source, like Azure Event Hub or Kafka, using appropriate connectors. I write a streaming query to process the incoming data in real-time. For output, I direct the processed data to a destination, such as Azure Data Lake or a database. I ensure the pipeline is fault-tolerant by enabling checkpointing and handling failures gracefully.”
- How do you guarantee data security in Azure Databricks?
You might also come across Databricks interview questions scenario based like this one.
“To ensure data security, I always integrate Azure Databricks with Azure Active Directory for access control. I encrypt data at rest using Azure-managed keys and ensure data in transit is encrypted with HTTPS or secure protocols. I also use VNet integration to isolate Databricks in a secure network. Private endpoints and firewall rules are implemented to restrict access to authorised users only.”
Advanced Interview Questions on Azure Databricks
Here are some advanced Azure Data Bricks interview questions and answers.
- What are the different cluster modes available in Azure Databricks, and when would you use them?
Azure Databricks offers three cluster modes:
- Standard Mode: Used for most analytics and data processing tasks.
- High Concurrency Mode: Designed for workloads with multiple users, such as interactive notebooks or dashboards.
- Single Node Mode: Suitable for small-scale development or testing that doesn’t need distributed computing.
- How do you handle skewed data in Azure Databricks?
“To handle skewed data, I use techniques like salting. This involves adding random keys to the skewed data to distribute it evenly. Partitioning the data properly and using Spark’s repartition or coalesce can also help balance the load.”
- What is Databricks File System (DBFS), and how is it used?
DBFS is a distributed file system built into Azure Databricks. It allows seamless integration with Azure storage. I use DBFS to store data files, scripts, and machine learning models. It is accessible from notebooks, jobs, and libraries.
Azure Databricks Technical Interview Questions
Now, let’s take a look at some technical Azure Databricks interview questions and answers.
- How does Azure Databricks handle data versioning in Delta Lake?
Delta Lake supports data versioning with its transaction log. Each change creates a new version, allowing users to query or revert to previous states. I can use DESCRIBE HISTORY to view the versions and TIME TRAVEL to access historical data.
- What are the key differences between managed and unmanaged tables in Azure Databricks?
Managed tables are fully controlled by Databricks, including their storage. If a managed table is dropped, its data is deleted. Unmanaged tables, however, store data externally, and only metadata is managed by Databricks. Dropping an unmanaged table does not delete its data.
- How do you monitor and debug Spark jobs in Azure Databricks?
“I use the Spark UI to monitor job stages, tasks, and execution details. It provides insights into task durations, resource usage, and bottlenecks. For debugging, I review logs available in the UI and check the cluster event timeline for errors.”
Azure Databricks PySpark Interview Questions
Here are some commonly asked PySpark Databricks interview questions and answers.
- What is PySpark, and how is it used in Azure Databricks?
PySpark is the Python API for Apache Spark. It allows users to write Spark applications using Python. In Azure Databricks, PySpark is used for distributed data processing, machine learning, and ETL tasks.
- How can PySpark handle missing data in a DataFrame?
PySpark provides methods like fillna() to replace missing values and dropna() to remove rows with null values. It also supports conditional handling using the withColumn() method for custom logic.
- How does PySpark support machine learning in Azure Databricks?
PySpark integrates with MLlib, Spark’s machine learning library. MLlib provides tools for classification, regression, clustering, and collaborative filtering. It is fully compatible with Azure Databricks for scalable machine learning workflows.
Also Read - Top 15+ PySpark Interview Questions and Answers (2025)
Azure Delta Lake Interview Questions
- What is Delta Lake, and how does it enhance data processing in Azure Databricks?
Delta Lake is a storage layer that adds ACID transaction support to data lakes. It enables reliable and scalable data pipelines with features like data versioning, schema enforcement, and efficient queries.
- What are the key differences between Parquet and Delta Lake?
Parquet is a file format for data storage, while Delta Lake is a storage layer. Delta Lake extends Parquet by adding features like ACID transactions, version control, and schema evolution.
- How does Delta Lake handle schema evolution?
Delta Lake allows schema evolution by adding new columns or modifying existing ones. This is done using the mergeSchema option during write operations. It ensures compatibility while maintaining data integrity.
Azure Databricks Interview Questions for Data Engineer
These are some important Azure Databricks interview questions and answers for data engineer.
- What is the role of a Data Engineer in Azure Databricks?
A Data Engineer in Azure Databricks is responsible for building and maintaining scalable data pipelines. They guarantee data integration, transformation, and storage in data lakes or warehouses. They also optimise performance and ensure data quality.
- How do you design ETL pipelines in Azure Databricks?
ETL pipelines are designed using Apache Spark and Databricks workflows. Data is extracted from sources like Azure Data Lake or SQL databases. It is then transformed using Spark transformations and loaded into the target destination.
- How do Data Engineers implement incremental data processing in Azure Databricks?
Incremental data processing is achieved using Delta Lake’s change data capture (CDC) features. Data Engineers use the MERGE operation to process only new or changed data, improving efficiency.
Azure Databricks Interview Questions Cognizant
These are some Azure Databricks interview questions and answers asked at Cognizant.
- How would you approach integrating Azure Databricks with other Azure services in a client project?
“To integrate Azure Databricks with other services, I would start by identifying the client’s data flow requirements. For example, Azure Data Lake can be used for storage, while Azure Synapse is ideal for advanced analytics. I would configure secure connections and ensure data pipelines use Azure Data Factory for orchestration.”
- What do you know about Cognizant’s use of Azure Databricks for client solutions?
“While I do not have direct experience at Cognizant, I understand that the company uses Azure Databricks for scalable data analytics and machine learning solutions. Cognizant likely integrates Databricks with Azure tools like Synapse and Power BI to provide comprehensive analytics platforms for clients.”
Also Read - Top 35+ Azure Data Factory Interview Questions and Answers
Wrapping Up
Azure Databricks is a powerful tool for data engineering, analytics, and machine learning. By reviewing these Azure Databricks interview questions, you can confidently prepare for your next big opportunity. Stay updated on the latest tools and trends to stay ahead in your career. Looking for the databricks jobs in India? Visit Hirist, the online job portal that connects you with top opportunities in the tech industry.
