Azure Databricks Interview Questions and Answers for Freshers and Experienced in 2023


Azure Databricks is a big data and analytics platform Microsoft offers in collaboration with Databricks. Azure Databricks is used extensively in many industries for data engineering, data analytics, and machine learning scenarios. In Azure Databricks, large datasets can be analysed for insights, data-driven apps as well as complex machine-learning models can be created. 

Azure Databricks is known for its ease of use and hence highly sought after among leading corporations. Hence data analytics questions based on Azure Databricks form a major part of many a corporation’s interview rounds. This blog about Azure Databricks questions and answers for experienced and freshers will help prepare well for job interview. 

Azure Databricks Questions and Answers fr Freshers

While preparing for a job in data science, you could consider taking up a Graduate Certificate Programme in Data Science from upGrad. This programme will equip you with the necessary skills to help you easily crack your interview and increase your employability. If you are a fresher preparing for an Azure Databricks interview check out these questions:

1. What advantages do Microsoft Azure Databricks offer? 

  • With its managed clusters, Azure Databricks reduces cloud computing costs by up to 80%. 
  • It enhances productivity through an intuitive user experience for building and managing data pipelines. 
  • The platform ensures data security through various measures, including role-based access control and encrypted communication.

2. What does the Databricks filesystem do? 

The Databricks filesystem stores data within Databricks, making it suitable for handling large workloads with significant data volumes. It is compatible with the Hadoop Distributed File System (HDFS).

3. What does “data plane” mean in Azure Databricks context? 

The data plane in Azure Databricks is the network segment responsible for data storage and processing. It encompasses the Apache Hive metastore and the Databricks filesystem, providing essential components for managing and organising data efficiently within the platform.

4. What is a “dataframe” in Azure Databricks? 

In Azure Databricks, a dataframe is a specialised table format used for data storage in the runtime environment. It offers full support for ACID transactions and is designed to provide efficient read and write operations, making data processing faster and more streamlined.

5. What is the purpose of widgets in Azure Databricks? 

Widgets are essential for creating dynamic notebooks and dashboards that can be re-executed with multiple parameters. They enable parameterisation logic testing while building notebooks and dashboards. Widgets add parameters to these elements, facilitating an exploration of query results with various inputs. 

The Databricks widget API supports creating diverse input widgets, retrieving bound values, and removing input widgets consistently across Python, R, and Scala, with slight variations in SQL.

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6. What is Kafka’s role in Azure Databricks? 

Kafka is the recommended tool for working with Azure Databricks’ streaming capabilities. It enables the ingestion of diverse data, such as sensor readings, logs, and financial transactions, making real-time processing and analysis feasible. 

Kafka is crucial in handling streaming data, allowing seamless integration with Azure Databricks for various data-driven applications and scenarios.

7. What distinguishes a cluster from an instance in Azure Databricks? 

In Azure Databricks, a cluster consists of multiple instances that execute Spark applications, while an instance refers to a VM running the Databricks runtime. Clusters are amalgamations of computational resources and configurations, enabling the execution of data analytics, engineering, and science workloads. Each cluster can accommodate several instances, providing scalability and performance for diverse data processing tasks.

8. What are the various types of cloud services provided by Databricks? 

Databricks offers a Software-as-a-Service (SaaS) solution, leveraging clusters to maximise Spark’s capabilities in managing storage. Users can easily configure applications without extensive changes before deployment. 

This SaaS-based platform allows seamless utilisation of Spark’s power for data processing and analytics, simplifying data management and enabling efficient application development.

9. What skills are required to use Azure Storage Explorer? 

Azure Storage Explorer is a standalone tool supporting Windows, Mac OS X, and Linux, allowing users to manage Azure Storage from any computer. Users need proficiency in navigating its intuitive graphical user interface to use this tool effectively. 

Knowledge of various Azure data stores, such as Blobs, ADLS Gen2, Tables, Cosmos DB, and Queues, is essential to efficiently access and interact with data using Azure Storage Explorer.

10. What is a Delta Lake table in Azure Databricks? 

In Azure Databricks, Delta Lake tables store data in delta format, extending existing data lakes. The delta engine, a core component, supports this format for data engineering, enabling modern data lakehouse/lake and lambda architectures. 

Delta Lake tables offer benefits like data reliability, caching, ACID transactions, and indexing. The delta lake format makes preserving data history straightforward, using methods like archive tables and slowly changing dimensions.

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Azure Databricks Questions and Answers for Experienced

1. What is Azure Serverless Database Processing? 

Azure Serverless Database Processing enables stateless code execution independent of physical servers. Users only pay for the resources used during programme execution, leading to a cost-effective system. 

The serverless model eliminates the need to manage server infrastructure, allowing developers to focus solely on application logic. This approach optimises resource allocation and reduces costs by billing users only for actual resource consumption during the execution period.

2. What sets Azure Databricks apart from AWS Databricks? 

Databricks is an independent open-source data management platform available on Azure and AWS. Azure Databricks is the result of integrating Databricks with Azure, while AWS Databricks is integrated with AWS. 

Azure Databricks offers more functionalities and popularity due to leveraging Azure AD authentication and other Azure services for enhanced solutions. AWS Databricks primarily serves as a hosting platform for Databricks and offers fewer functionalities than Azure Databricks.

3. What is a dataflow map in Microsoft’s data integration? 

A dataflow map is a code-free data integration experience called Mapping Data Flows, provided by Microsoft. It allows users to visually design data transformation flows without writing code. In contrast, Data Factory Pipelines offer a more complex data integration experience. Data flows are used to build Azure Data Factory (ADF) activities functioning within ADF pipelines, streamlining the data integration process.

4. Can Apache Spark efficiently distribute compressed data sources like .csv.gz files? 

Apache Spark’s default behaviour for reading zipped CSV files or serialised datasets is single-threaded. However, the data is stored in memory as a distributed dataset once read. Compressed files offer high safety levels and can be split into multiple extents with Azure Data Lake or other Hadoop-based file systems. This distribution may lead to potential bottlenecks depending on the number of files and threads used.

5. What are the types of cluster modes available in Azure Databricks?

When setting up an Azure Databricks cluster, one can choose from three cluster modes that best suit their needs, with no option to change modes later.

  • Standard Cluster: The default mode shared among multiple users without isolation. Suitable for Scala, R, Python, and SQL workloads but limited to single users. Requires at least one worker node, terminates after 2 hours.
  • High Concurrency Cluster: Supports table access control and provides fine-grained sharing for optimal resource use and low query latency. Supports Python, SQL, and R workloads.
  • Single Node Cluster: Executes Spark jobs on the driver node only. Like standard clusters, it automatically terminates after 2 hours.

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6. Can Databricks be integrated with a private cloud environment? 

Databricks can be deployed on Amazon Web Services (AWS) and Microsoft Azure cloud platforms. While Databricks leverages open-source Spark technology, it is not directly available for deployment in private clouds. 

However, one could create a self-hosted cluster in a private cloud. Opting for a private cloud deployment would mean foregoing the extensive administration tools and managed services that Databricks offers on AWS and Azure.

7. Can Azure Key Vault be used as a replacement for Secret Scopes? 

It is possible to consider Azure Key Vault as an alternative, though it requires some preparation. You can start by creating a key with restricted access and storing it in Azure Key Vault. Unlike scoped secrets, updates to the key value won’t affect the scoped secret. 

Azure Key Vault usage has several benefits, especially when managing secrets across multiple environments. While it may require some time and effort to set up, Azure Key Vault offers a secure and centralised solution for managing secrets in Azure Databricks.

8. How does Azure SQL DB ensure data security? 

Azure SQL Database is a managed, scalable database service on Azure. It employs various data protection measures for data stored in it. 

  • SQL server firewall rules and database-level firewall rules prevent unauthorised access. 
  • Transparent Data Encryption (TDE) encrypts data at rest, transactions, and backups in real-time.
  • Azure SQL DB offers an audit feature for setting audit policies at the database or server level, ensuring comprehensive data security and compliance.

9. What is Mapping Data Flow in Azure Data Factory? 

Mapping Data Flow is a visual data transformation feature in Azure Data Factory (ADF). Data engineers use it to design data transformation logic without coding. These transformations are executed within ADF pipelines as activities, leveraging optimised Apache Spark clusters. 

Mapping data flow benefits from ADF’s scheduling, flow control, and monitoring capabilities. With no coding required, data engineers can easily integrate data using a user-friendly visual interface. ADF-managed execution clusters handle data flow processing, managing aspects like code translation, path optimisation, and data flow job execution for efficient data integration.

10. What is serverless data processing? 

Serverless data processing refers to building and running applications without managing underlying servers. It allows developers to focus solely on application development while the serverless platform handles server management. With serverless computing, data processing applications run independently on user devices or servers. 

These apps offer simpler, faster, and more efficient data processing. Users only pay for computing resources used during app execution, even for short durations. This cost-effectiveness makes serverless data processing advantageous, enabling significant savings by paying only for used resources.

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Practicing interview questions will brush up your basics and help you gauge what interviewers may expect in an interview. 

Check out the Master of Science in Data Science from LJMU offered by upGrad to understand the concepts of Azure Databricks better and equip yourself with other necessary skills to prepare you for the industry. This course is provided in collaboration with Liverpool John Moores University and offers dedicated career assistance. 

How can freshers prepare for an Azure Databricks interview?

Freshers can prepare for interview questions on Azure Databricks by understanding the platform's features, practising with hands-on projects, and reviewing Apache Spark concepts. Additionally, they should familiarise themselves with supported languages, data ingestion, and performance optimisation techniques.

Are there any specific tips or strategies for effectively answering Azure Databricks interview questions?

Here are some strategies you can employ while answering Azure Databricks interview questions effectively: Be familiar with key concepts Demonstrate practical experience Emphasise data transformation skills Review common interview questions Discuss best practices

What are some real-world scenarios where Azure Databricks can be used, and how can candidates showcase their understanding of these scenarios during an interview?

Large-scale data processing, real-time data streaming, advanced analytics, and machine learning projects are just a few examples of practical applications for Azure Databricks. Candidates can demonstrate their knowledge if asked Databricks interview questions, scenario based by outlining how they used Databricks in previous projects or coursework to manage big data workloads, develop data pipelines, execute data analysis, and build machine learning models to address business difficulties.

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