Azure Databricks Interview Questions and Answers for 2025

Azure Databricks allows you to store your confidential information (e.g., credentials) in notebooks or jobs which you can retrieve later via referencing them. A secret in Azure Databricks refers to a key-value pair that contains some secret information. This secret information has a unique key name inside a secret scope. A maximum of 1000 secrets can be stored in one secret scope, and each secret can be a maximum of 128KB.

Secret names are not case-sensitive. A secret can be referred to using a Spark configuration property or a valid variable name. You can create secrets using the CLI and REST API. Before creating a secret, you must consider which secret scope you are using. The process of creating a secret varies for different secret scopes. You can read a secret using the Secrets utility in a job or notebook.

Any Azure Databricks identity is verified using credentials. In Azure Databricks, credentials may be a username & password or personal access token. A personal access token is basically a means to authenticate an Azure Databricks entity (a user, group, or service principal) as it helps Azure Databricks verify the identity of the entity.

Creating an Azure Databricks personal access token just takes you a few steps: 

• Click your username present at the top bar in your Azure Databricks workspace. From the drop-down, choose User Settings. 
• Click Generate new token on the Access tokens tab. 
• Click Generate. 
• Once you copy the token, click Done. 

Databricks is an independent, open-source data analytics platform. It is currently optimized to run only on the two public clouds: AWS and Azure. This is because these two platforms are optimized to provide better integration and performance with Databricks features. Databricks has an official agreement and feature integration with these platforms to provide Databricks services so you get more features and optimized performance on these cloud platforms. Data management requires better integration and optimized platforms to offer quality data analytics services.

As Databricks is an open-source, free data analytics platform, you can set up and run your own Databricks cluster on-premises or on a private cloud infrastructure. However, the kind of features you get on Azure and AWS will not be available on that cluster running on the private cloud. If you require more advanced capabilities and control over data workloads, Azure Databricks is the best option followed by AWS Databricks.

This question is a regular feature in Azure Databricks interview questions for a data engineers, be ready to tackle it. Azure Databricks has various components that help in its functioning. However, some components that play a pivotal role include 

• Workspace: Developers mainly use Databricks using interactive and collaborative workspaces. A workspace is a notebook-based environment with the following features: 
  • In-built version control and integration features with Git/Github. 
  • Enterprise-level security. 
  • ML lifecycle management from development to production. 
  • Query visualizing, algorithm building, and dashboard generation. 
• Apache Spark: It is an open-source processing engine that is responsible for memory-based data processing. Spark is the basic component of Azure Databricks that handles queries and workloads on the Databricks platform. It is widely known to work well with processing large data and machine learning.  
• Managed Infrastructure: Managed clusters are a part of the managed infrastructure. A cluster is a set of VMs that fasten the delivery of results by distributing work. You can create customized clusters with popular data analytics and data science libraries to meet your specific requirements. You can auto-scale your clusters when required to meet the volume demand with just a click.  
• Delta: It is an open-source file format that was designed to solve the challenges posed by standard delta lake file formats. Delta is built using a columnar format, known as Parquet, that is used for large data applications with transaction logs and metadata. 
• ML Flow: It is an open-source machine language framework created for managing the ML lifecycle. It solves the major challenge of implementing ML in production. 
• SQL Analytics: It is a part of Azure Databricks that serves as a home within Databricks for SQL analysts. SQL Analytics is driven by SQL Endpoints. These endpoints are spark clusters specifically designed for SQL workloads. You can connect to these endpoints using BI tools and SQL Analytics UI to access data from your data lake. 

Yes, Azure Key Vault can serve as an alternative to secret scopes. Azure Key Vault is a storage that can store any confidential information. To use Azure Key Vault as an alternative, you need to set it up first.

Create a key value that you want to save in Azure Key Vault with restricted access. It is not required to update the scoped secret even if the value of the secret has to be modified in any way. Using Azure Key Vault as an alternative to secret scopes has many associated benefits, of which the most important one is getting rid of the headache of keeping track of secrets in multiple workspaces at the same time.

When an Azure Databricks user anticipates a predetermined amount of workloads they may get on Azure Databricks in advance and wants to reserve Azure storage to meet the workload requirements, they can do so with the reserved capacity option. Azure provides this option for Azure users who are keen on saving storage costs without compromising on service quality.

With this option, Azure users are assured of uninterrupted access to the amount of storage space they have already reserved. Azure provides two cost-effective storage solutions (Azure Data Lake and Block Blobs) that can store Gen2 data in a standard storage account.

Azure Databricks offers autoscaling for catering to dynamic workloads. The amount of dynamic workloads is never the same and has varying spikes in workload volumes. Autoscaling allows Azure clusters to meet variable workloads by automatically scaling up or down as per the amount of workload.

Autoscaling not only improves resource utilization but also saves the costs of resources. Resources are scaled up when the volume of the workload rises and scaled down when the volume of the workload drops. Resources are created and destroyed in Azure clusters based on the volume of workloads, so autoscaling is a smart way to manage workloads efficiently.

Formerly known as SQL data warehouse, the dedicated SQL pool refers to a standalone service running outside of an Azure Synapse workspace. Though the dedicated SQL pool runs outside Azure Synapse, it is a part of Azure Synapse Analytics. It is a set of technologies that allows you to use the platform for enterprise data warehousing. Dedicated SQL pools assist you in improving the efficiency of queries and reducing the amount of data storage required by storing data in both relational and columnar tables.

Azure Synapse is an analytics service on the Azure platform that integrates Big Data analytics and data warehousing. You can consider Azure Synapse as an evolved form of SQL data warehouse. Azure Synapse SQL (DWU) helps you provision resources in Data Warehousing Units (DWU).

Handling issues effectively while working with Azure Databricks is an essential skill to have for data engineers and data scientists. If we face any problem while performing any task on Azure Databricks, we must go through the official Azure Databricks documentation to check for possible solutions.

Azure Databricks documentation has a list of common issues one can face while working on the platform along with their solutions. With detailed step-by-step procedures and other relevant information, you can troubleshoot problems easily.

If the documentation does not help and you require more information, you can connect with the Databricks support team for further assistance on your issue. The support team has knowledgeable staff who will guide you on how to solve the problem.

A data lake in Azure Databricks refers to a pool of raw data of all types. This includes unstructured, old, and raw data collected for a purpose that is yet to be determined. Data lakes are a cheap option to store and process data efficiently. They can store data in any format of any nature.

A data lakehouse in Azure Databricks is an advanced data management architecture that integrates the features of data lakes with the ACID transactions and data management of data warehouses. It combines the flexibility and economical features of data lakes with the data management features of data warehouses to implement machine learning (ML) and business intelligence (BI) on all data.

Yes, code can be reused in the Azure notebook. To reuse code, we must first import the code into your notebook from Azure notebook. Without getting the code in your notebook, it cannot be reused. 

We can import code in two ways: 

• Code resigning in a different workstation: If the code is present on a different workstation, we should create a component (module/jar file) for the code first before we integrate it into the module/jar file. 
• Code present on the same workstation: If the code is present on the same workstation, importing is easier and simpler. We can use the code right away once we import it.

Azure Recovery Services Vault (RSV) is a storage entity on the Azure platform that stores backup data. Backup data consists of a wide range of data, including multiple copies of data and configuration information for VMs, workstations, workloads, and servers. By using Azure RSV, you can store backup data for many Azure services and organize backup data in a better way. This helps minimize backup data management overhead.

You can use Azure RSV along with other Azure services, including Windows Server, System Center DPM, and Azure Backup Server. Azure RSVs follow the Azure Resource Manager model. Azure RSV comprises Azure Backup and Azure Site Recovery. Azure Backup replicates your data to Azure as a backup. Azure Site Recovery provides a failover solution for your server when your server is not live.

With Azure RSV, you can do the following:

• Protect backup data: You can secure your cloud backups and restore them later when required.
• Monitor your hybrid IT environment: You can centrally monitor your (on-prem as well as cloud) IT environment using the Azure RSV portal.
• Cross Region Restore: You can pair your Azure region with other regions to restore your backups there. This is called cross region restore.

In Azure Databricks, workspaces are functional instances of Apache Spark that classify objects like experiments, libraries, queries, notebooks, and dashboards into folders. They provide access to data, jobs, and clusters, thereby serving as an environment for accessing all Azure Databricks assets.

Workspaces can be managed using Databricks CLI, workspace UI, and Databricks REST API reference. The workspace UI is most commonly used to manage workspaces. There can be multiple workspaces in Azure Databricks for different projects. Every workspace has a code editor, a debugger, and Machine Learning (ML) & SQL libraries as its main components. Multiple other components exist in a workspace that performs different functions.

Data is imported from Azure Data Factory into Azure for the big data pipeline and is stored in a data lake. Azure Databricks reads data from several resources and transforms it into actionable insights.

No, Azure Databricks officially cannot be managed via PowerShell. This is because PowerShell is not compatible to work with Azure Databricks. We can use other commonly used methods like the Azure portal, the Databricks REST API, and the Azure Command Line Interface (CLI) for Azure Databricks administration. However, it supports PowerShell modules that can be used for this purpose.

Among all these methods, the Azure portal is the simplest one to use, followed by Azure CLI. Managing Azure Databricks using the Databricks Rest API is very complex and requires some level of expertise. Due to its complexity, most data engineers avoid using Databricks REST API and use the other two methods for managing Azure Databricks.

Azure Command-line Interface (CLI) is a powerful tool that helps you connect to Azure and execute administrative commands for managing Azure resources. Commands are executed using interactive scripts through a terminal.  

Azure Databricks CLI can help you perform the following tasks in Azure Databricks: 

• Provision resources: You can use Azure Databricks CLI to provision compute resources in Databricks clusters. 
• Create and run tasks: You can create and run data processing and data analysis tasks. 
• Manage notebooks: You can easily manage notebooks (list, import, and export them) and folders in a workspace. 
• Troubleshoot issues: Azure CLI helps you troubleshoot technical issues easily. 

Delta Lake enhances data reliability by providing ACID transactions, scalable metadata handling, and schema enforcement. It improves performance with optimized storage and query execution, making it suitable for large-scale data processing and analytics.

Azure Databricks integrates with Azure Synapse Analytics by allowing users to move data seamlessly between the two platforms. This integration enables advanced analytics and data warehousing capabilities, combining the best of big data and traditional SQL analytics.

Azure maintains multiple copies of the data stored in it at different levels to ensure that the data is available and accessible all the time. Azure storage facilities have a number of data redundancy solutions that ensure data security and availability. Each solution is tailored to meet specific requirements of Azure customers such as the time to retrieve replicas and the importance of data being replicated.

1. Locally Redundant Storage (LRS): To keep data highly available, Azure replicates data in multiple storage areas located in the same data center. As the copies of data are stored in three different places in the same physical location, it is also referred to as locally redundant storage (LRS). This is the most economical solution to ensure data redundancy.  
2. Zone Redundant Storage (ZRS): Storage data is replicated to three different availability zones (AZs) of the primary zone. Data is copied to these three AZs to ensure that if the primary site becomes unavailable, data can be retrieved from the copies stored at these AZs. This data redundancy feature is called zone redundant storage because data is stored in different data centers of the same region. 
3. Geographically Redundant Storage (GRS): Storage data is replicated to data centers located in different geographical regions. Azure provides this data redundancy option for the cases where the entire region becomes unavailable. Copies of data are stored in at least two distinct locations across different geographies. A geo-failover is required to access data from the secondary location in the case when the primary location is unavailable. 
4. Read Access Geo Redundant Storage (RA-GRS): This data redundancy option ensures that the data stored in the secondary region can be accessed when the primary region is down. 

Choosing a method for transferring data depends on some important factors that must be considered. These factors help you decide which method would be more suitable for the data you want to transfer from on-premises to Azure. The factors you must consider for data transfer include 

• Data size 
• Network bandwidth 
• Data transfer frequency (periodic or one-time transfer) 

You can transfer data from on-premises to Azure in two ways: 

1. Offline transfer via devices 

Offline data transfer is best suited when you want to transfer a large amount of data in one go. For offline data transfer, you can use large discs or other storage devices supplied by Microsoft Azure or send your own discs to Azure. Some of the devices that you can use for transferring data include Azure data box heavy, Azure data box,  and Azure Import/Export. 

1. Data transfer over a network 

Data can also be transferred from on-premises to Azure over a network using the following methods:  

1. Graphical interface: This data transfer method is suitable when you have a few files to transfer without using automation. You can use graphical interface tools such as Azure Storage Explorer to transfer data from on-premises to the Azure cloud. 
2. Programmatic or scripted transfer: Scripted data transfer involves using software tools provided by Azure to transfer data from on-premise to Azure. Some software tools for programmatic transfer are Azure PowerShell, AzCopy, and Azure CLI. You can use SDKs for PHP, Ruby, Python, Java, and Node/JS to transfer data programmatically. 
3. On-premises devices: Physical or virtual devices supplied by Azure can be used for data transfer. These devices will be at your data center and help in fast data transfer over a network connection. Azure's physical device for data transfer is Azure Stack Edge. Azure Data Box Gateway is a virtual device that helps in fast data transfer. 
4. Managed Data pipelines:  Managed data pipelines can help you transfer data over a network connection frequently from your data center to Azure. These data pipelines can be set up and managed using Azure Data Factory. 

Azure Cosmos DB supports five consistency models or levels to provide enhanced performance and high availability of data stored. Cosmos DB provides customers with 100% consistency of the read requests for the selected consistency model. 

1. Strong consistency: This consistency model guarantees linearizability. This means all requests are served concurrently, and the reads will return the most recent version of an item. It enables users to read only the latest committed write. This consistency model is most expensive. 
2. Bounded staleness consistency: In this consistency model, reads from a secondary region may not return the latest version of a data item globally. However,  reads return the latest version of the data in that region. This model works well in cases where consistency and availability are not priority.   
3. Session consistency: For Azure Cosmos DB, Session consistency is the default model. It is the most widely used mode across all regions. When a user goes to the location where a write was executed, they are guaranteed to get the most recent version of that data. It has the fastest throughput for reading and writing operations.   
4. Consistent prefix consistency: In this model, single document writes have eventual consistency. Users will not see out-of-order writes in this consistent prefix consistency model. However, data here is not replicated across different regions at a fixed frequency. 
5. Eventual consistency: This model provides the weakest consistency of all. This model provides no assurance that data is replicated within a set amount of time or a set version. Users may read data that is older than what they had read before. It offers the highest read latency. 

In Azure Data Factory, visually designed data transformations are called mapping data flows. Data engineers use mapping data flows to develop data transformation logic without scripting. With data transformation logic, data flows are executed inside Azure Data Factory (ADF) pipelines as activities. These pipelines use optimized Apache Spark clusters. Data flow activities can be invoked with the help of existing capabilities of Azure Data Factory such as scheduling, flow, control, and monitoring.

Without any coding, mapping data flows offer an impressive visual experience. ADF-managed execution clusters run data flows for scaled-out data processing. ADF manages all important aspects like code translation, path optimization, and data flow job execution. Mapping data flows are used by data engineers for data integration with no coding involved.  

You may face many critical challenges for continuous integration/continuous delivery while building a data pipeline. However, some challenges are critical and worth mentioning such as  

• Data exploration: you may find it difficult to explore data while building a data pipeline when multiple users collaborate on the same project. 
• Iterating unit tests: Changing code and writing unit cases iteratively pose a challenge as the process becomes cumbersome. 
• Continuous build and integration: New code is continuously merged. The build server needs to pull the latest changes and perform unit testing for multiple components and publish the artifacts on an ongoing basis.  
• Staging data pipelines: Pushing data pipelines to the staging environment poses a challenge as the pipeline is tested against a much larger data set (which is similar to production data) for data quality and performance. 
• Pushing data pipelines to production: Pushing data pipelines from the staging environment to the production environment may often pose challenges.

Git and Microsoft Team Foundation Server (TFS) are collaborative and version controlling tools. They both help you manage code easily. When it comes to Azure Databricks, working with TFS is not supported. As of now, you can use only Git or a similar repository system with Azure Databricks. Git is a free, open-source repository that allows users globally to manage more than 15 million lines of code but Team Foundation Server (TFS) has comparatively less capacity than Git. It has the capacity to handle 5 million lines of code.

Azure Databricks notebooks can easily integrate with Git. For managing Databricks code easily, we need to create a Databricks notebook, upload the notebook to Git, and then update it when required. We can consider the Databricks notebook as a replica of our project.

Azure Data lake (ADL) Gen2 employs a comprehensive and robust security mechanism to protect stored data. Its security mechanism has six layers of protection. 

1. Authentication: For authentication, it uses Azure Active Directory (AD), Shared Access Token, and Shared Key to keep user accounts secure. 
2. Access control: It uses access control lists (ACLs) and roles for granular control over who is entitled to access which resources. 
3. Network isolation: It allows admins to isolate networks logically by using firewalls to accept or refuse network traffic from specific IP addresses or VPNs .  
4. Data protection: As part of the data protection security measure, it encrypts data in transit via HTTPs to secure confidential information. Also, it encrypts data at rest. 
5. Advanced threat protection: This layer helps in monitoring any attempts to exploit or access the storage account. It will alert in case of any malicious activity detected. 
6. Auditing: All management activities are logged by ADL Gen2. ADLS logs will provide information on activities that took place in your account. This auditing capability will help in identifying malicious activities and trace threat actors.

Yes, we get the access control feature with Azure Delta Lake for enhanced security and governance. We can use the access control lists (ACLs) to restrict user access to workspace objects, pools, tasks, dashboards, clusters, tables, schemas, etc. Workspace objects include notebooks, models, folders, and experiments.

This access control feature prevents unauthorized access to Azure Delta lake and protects the data stored in it. Admins and selected users having delegated ACL management rights can manage the access control lists. Access control can be enabled or disabled for workspace objects, clusters, data tables, pools, jobs by admin users at the workspace level.

Azure Data Factory pipelines can be run either manually or through a trigger. An instance of pipeline execution in Azure Data Factory is defined as a pipeline run. These pipelines can be programmed to run automatically on a trigger or in response to external events.

Below is the list of  triggers that can make Azure Data Factory pipelines run automatically: 

1. Schedule trigger: This trigger executes a Data Factory pipeline run at a set time or schedule.  
2. Tumbling window trigger: This trigger executes at a periodic interval without termination. It retains the previous state. 
3. Event-based trigger: This trigger executes a pipeline run in a response to an event. 

Cloud object storage like Azure Blob offers a simple, scalable, and cost-effective solution to store important data on the cloud. Though data replication improves the availability of data stored in Azure Blob storage, it does not completely eliminate the need of having a backup.  

Azure Blob backups are important to handle incidents where the entire cloud storage is damaged. Data retrieval is possible only via backups. Consider some scenarios where Azure Blob backups can safeguard data stored in the Blob storage: 

1. Safeguard against accidental or malicious deletion: Cloud storage can be deleted accidentally or as a result of malicious activities. Having a backup can help you retrieve data and avoid data loss.  
2. Ransomware: If the cloud storage is attacked by ransomware, backups can help you restore the data without paying ransom. 
3. Data corruption and recovery point objectives: If data stored in cloud storage gets corrupted, you can restore to the previous known healthy state of data with the help of backups. 

A DataFrame is a specified form of tables that are used to store data inside Databricks runtime, whereas Pandas is a free-to-use Python package popular for machine learning and data analysis tasks. Apache Spark DataFrames are different from Pandas. Though DataFrames function in a manner similar to Pandas, they have differences.

In Apache Spark, Pandas cannot be used as an alternative to DataFrames. Being native to Azure, DataFrames get an edge over Pandas. Moving between these two frameworks will impact the performance, so users of DataFrames and Pandas can switch to Apache Arrow to improve performance and get better features and functionalities.

You can import data into Azure Delta lake from cloud storage using two ways: 

• Auto Loader: It provides a structured streaming source (CloudFiles) to process files that come in Azure cloud storage. It effectively and incrementally processes data without any additional setup.  
• COPY INTO: It helps SQL users to load data from cloud object storage to Azure Delta lake tables. This method can be used in Databricks notebooks, SQL, and Databricks jobs. 

You must consider a few things to decide which method is best for you: 

• If you want to ingest millions of files, you can use Auto Loader. If you have a few thousand files to ingest, you can go with COPY INTO. This is because Auto Loader is cheaper and more efficient to handle large amounts of files. 
• If you have a dynamic data schema that changes frequently, Auto Loader suits you best as it offers primitives around schema evolution and inference. 
• You can use COPY INTO for loading a subset of reuploaded files efficiently as it makes the process easier. 

Before developing business analytics logic, you must have the Databricks code written by other team members in your notebook first. Having the code in your notebook will help you reuse it to build business analytics logic. For copying the code to your notebook, you need to import it.  

You can use two methods for importing the code based on the place the code is present: 

• You can import the code and start using it if the code lies in the same workspace. 
• If the code is in another workspace, you have to make a jar or module file and then import it to your workspace. 

The process of dividing a huge dataset (DataFrame) into multiple small datasets while writing to disk (based on columns) is called PySpark Partition. On a filesystem, data partitioning can help to improve the performance of queries while dealing with large datasets in the Data lake. This is because transformations on partitioned data run smoothly and quickly, so the speed of query execution is improved.

There are two partitioning methods supported by PySpark:

• Partition in memory: Using this method, partition or repartition the DataFrame by invoking coalesce() or repartition() transformations.
• Partition on disk: You can partition data while writing the DataFrame to disk based on columns using PartitionBy().

A PySpark DataFrame refers to a distributed group of structured data in Apache Spark. PySpark DataFrames are equivalent to relational database tables or an Excel sheet. They are comparatively more optimized than R or Python. You can create PySpark DataFrames using multiple sources such as Hive Tables, Structured Data Files, existing RDDs, and external databases.

You can create PySpark DataFrames using three methods: 

• You can use data formats like XML, CSV, JSON, etc. 
• You can import data from existing RDDs. 
• You can use programmatically specifying schema. 

DataFrames have some characteristics in common with Resilient Distributed Datasets (RDDs). 

• Immutable: Once DataFrames are created, they cannot be changed but can be transformed after applying transformations. 
• Distributed: Like RDDs, DataFrames are distributed in nature. 
• Lazy evaluations: Lazy evaluations mean that no task is executed until you perform an action. 

Best practices for securing data in Azure Databricks include using private link for secure connections, configuring network security groups (NSGs), implementing least privilege access with RBAC, encrypting data at rest and in transit, and regularly auditing and monitoring access logs.

Azure Databricks supports real-time data analytics by integrating with Apache Kafka and Azure Event Hubs for streaming data ingestion. It leverages Spark Structured Streaming to process and analyze streaming data in real-time, enabling use cases like fraud detection and dynamic pricing.

Recent updates in Azure Databricks include enhancements in Delta Lake, new machine learning tools and libraries, improved integration with Azure Synapse, performance optimizations, and advanced security features. Staying updated with these features helps professionals leverage the latest capabilities.