Your Ultimate Guide to Azure Data Engineer Interview Preparation

Top Azure Data Engineer Interview Questions & Answers 

Azure data engineer interview questions are created to analyze the breadth and depth of your technical knowledge and skills, problem-solving skills and knowledge of data infrastructure in the cloud environment. Explore the various data engineering Azure interview questions that will help you prepare well and demonstrate your skills. The top Azure data engineer interview questions and answers are as follows:

1. Explain Microsoft Azure.

It is a cloud computing platform that offers both software and hardware. The service provider provides a managed service that allows users to access the services on demand.

2. What data masking features are accessible in Azure?

Data masking in Azure is crucial for data security. It restricts crucial and sensitive information to certain groups of users.

• It is accessible for Azure SQL Managed Instance, Azure SQL Database and Azure Synapse Analytics.
• It can be used as a security policy on each SQL database across an Azure subscription.
• Users get to control the masking level according to the requirements.
• It masks only the query results for certain column values on which data masking is applied. It doesn’t affect the data stored in the database.

3. What do you understand about Polybase?

Polybase supports T-SQL and optimizes data ingestion into PDW. It lets developers query external data from the supported data stores regardless of their storage architecture.

Polybase is used for:

• Query data stored in Azure Blob Storage, Hadoop or Azure Data Lake store from Azure Synapse Analytics or Azure SQL Database. It eliminates the requirement to import data from any external source.
• Import data from Azure Blob Storage, Hadoop, or Azure Data Lake Store without installing a third-party ETL tool only using certain simple T-SQL queries.
• You can export data to Azure Data Lake Store, Hadoop, or Azure Blob Storage. It also supports archiving and exporting data to external data stores.

4. What do you understand about reserved capacity in Azure?

Microsoft offers a reserved capacity option for Azure storage to optimize costs. Considering the reservation period on the Azure cloud, the reserved storage offers customers a specific capacity amount. Azure Data Lake and Block Blobs are available to store Gen 2 data in a standard storage account.

5. How can you ensure compliance and data security with Azure Data Services?

Implementing Azure Active Directory ensures data security by identifying RBAC and allowing management to restrict access based on the principle of the least privilege. Azure Policy is also used to enforce compliance requirements and organizational standards. For GDPR compliance, Azure compliance offerings are leveraged, ensuring data practices align with EU standards.

6. Elaborate on your experience with Database design and Data modeling in Azure.

For this question, elaborate on your experience with Cosmos DB, Azure SQL Database and other Azure data storage services. Moreover, explain your approach towards indexing, normalization, and partitioning in terms of scalability and performance.

Example: For a high-traffic e-commerce website, I created a data model by implementing Azure SQL Database. Furthermore, to remove redundancy, I focused on normalization, and to enhance query performance, I implemented partitioning strategies. In addition, to enhance speed searches on large datasets, I used indexing, which improved the application's response time.

Did You Know? 🔍

The average salary for an Azure Data Engineer in India is around INR 8,00,000 to INR 12,00,000 per annum.

7. How did you handle processing and data transformation in Azure?

For this question, elaborate on your experience with Azure Databricks, Azure Data Factory, or Azure Synapse Analytics.

Example: To orchestrate ETL pipelines, I used Azure Data Factory and leveraged Azure Databricks for complex data processing, through which I performed transformations via Spark. This enabled real-time analytics and streamlined data workflows.

8. Explain how to optimize and monitor Azure data solutions for performance.

For this scenario, explain how you use Azure SQL Database and Azure Monitor’s performance insights to track performance metrics.

Example: I use Azure monitor and application insights to monitor Azure data solutions, and I depend on performance insights to find bottlenecks for SQL databases.

9. How did you approach high availability and disaster recovery in Azure?

For the scenario, elaborate on the importance of high availability and disaster recovery planning.

Example: To assure high availability, I created a disaster recovery strategy via geo-replication of Azure for Azure SQL databases. 

10. What was your experience with data integration in Azure?

Discuss your experience with Logic Apps or Azure Data Factory for data integration. 

Example: I have integrated several data sources using Azure Data Factory. 

11. How have you used Azure's data analytics services to offer insights to the stakeholders?

Explain your experience with Power BI, Azure Synapse Analytics or Azure Analysis Services.

Example: I used Azure Synapse Analytics to aggregate data from multiple sources into one analytics platform. Later, I created Power BI dashboards, which offered stakeholders insights into sales trends and customer behavior to enable data-driven decision-making.

12. What process do you follow to troubleshoot issues in Azure data pipelines?

Discuss your methods to identify, diagnose and resolve data pipeline issues.

Example: To troubleshoot Azure data pipelines, I consult Azure Monitor longs to identify the problem. For challenging problems, I implement Log Analytics to analyze and query detailed logs.

13. What service will you implement to create a data warehouse in Azure?

Azure Synapse is an analytics service that combines enterprise, data, warehousing and big data analytics. It allows users to query data on individual terms using provisioned resources or serverless on-demand resources at scale.

14. Explain the Azure Synapse Analytics architecture.

Synapse SQL is designed to function with massive amounts of data, such as millions of rows in a table. It processes complex queries and returns the results within seconds, even with huge amounts of data. Synapse SQL functions on a massively parallel processing architecture that allocates data processing across several nodes.

15. Differentiate between Azure Synapse Analytics and ADLS.

The differences between ADLS and Azure Synapse Analytics are as follows:

16. Explain dedicated SQL Pools.

Dedicated SQL Pools are a collection of features enabling the implementation of traditional enterprise data warehousing platforms through Azure Synapse Analytics.

17. How to capture streaming data in Azure?

Azure offers a dedicated analytics service called Azure Stream Analytics. This service uses simple SQL and lets developers extend the query language by defining more Machine Learning functions.

18. Mention the different windowing functions in Azure Stream Analytics.

In Azure Stream Analytics, a window blocks the time-stamped data of an event, enabling users to perform multiple statistical operations on the event data.

The different windowing functions in Azure Stream Analytics are:

• Tumbling Window
• Hopping Window
• Sliding Window
• Session Window

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19. Mention the types of storage in Azure

The types of storage in Azure are listed below:

• Azure Blobs
• Azure Queries
• Azure Files
• Azure Disks
• Azure Tables

20. Define Azure storage explorer and mention its uses.

It is a versatile application that handles Azure storage for multiple platforms. It is for Mac OS, Linux, and Windows. The application offers access to several Azure data stores and an easy-to-use GUI. Azure storage lets users work even after being disconnected from the Azure cloud service.

21. Define Azure table storage.

It is optimized to store structured data. Table entities in structured data are basic data units equal to rows in relational database tables. Every entity represents the properties and key-value pair for table entities as:

• PartitionKey
• RowKey
• TimeStamp

22. What do you understand about serverless database computing in Azure?

In a computing situation, the program code resides on the client or the server. However, serverless computing implements the stateless code nature. Hence, the code doesn’t need any infrastructure.

23. What are the security options available in Azure SQL DB?

The data security options in Azure are as follows:

• Azure SQL Firewall Rules
• Azure SQL Always Encrypted
• Azure SQL Transparent Data Encryption 
• Azure SQL Database Auditing

24. Explain data redundancy in Azure.

Azure retains multiple data copies to offer high data availability. Clients can access certain data redundancy solutions according to the duration and criticality necessary to offer access to the replica.

• Locally Redundant Storage: Data gets replicated across multiple racks in a similar data center under this type.
• Zone Redundant Storage: This ensures that data gets replicated in three zones inside the primary region.
• Geo-Redundant Storage: It ensures that data is replicated across 2 regions and can be recovered if one complete region moves down.
• Read Access Geo Redundant Storage: It is quite similar to GRS but with an option to read access to the data in the secondary region if failure occurs in the primary region.

25. How do you ingest data from on-premise storage to Azure?

The major factors to consider when selecting a data transfer solution are:

• Network Bandwidth
• Data Transfer Frequency
• Data Size

Based on these factors, data movement solutions are:

Offline transfer: This is utilized for one-time data transfer in bulk.

Network transfer: In a network transfer, data transfer is performed in the following ways:

• Graphical interface
• Programmatic transfer
• Managed data factory pipeline
• On-premises devices

26. Mention the best ways to migrate data from on-premise databases to Azure.

To transfer data from the present on-premises SQL server to the Azure database, Azure offers the following choices:

• Azure SQL Database 
• SQL Server Stretch Database
• SQL Server on a Virtual Machine
• SQL Server Managed Instance

"Cloud is not just a technology, it's a business transformation." – Satya Nadella, CEO of Microsoft 🎯

27. Explain multi-model databases.

Azure Cosmos DB is a premier NoSQL service offered by Microsoft on Azure. It is the first multimodel, globally distributed database provided on the cloud by a vendor. The database can be used for data storage in multiple data storage models, including document-based, column-family-based, graph-based and key-value pairs. Regardless of the customer's chosen data model, global distribution, low latency, consistency, and automatic indexing characteristics are the same.

28. Explain the Azure Cosmos DB synthetic partition key.

Choosing a good partition key capable of distributing the data evenly across several partitions is important. A synthetic partition key can be created when there is no right column with appropriately allocated values. There are three ways to make a synthetic partition key:

• Random suffix: A random number gets added to the end of the partition key value.
• Concatenate Properties: Combining several property values to create all synthetic partition key
• Pre-calculated suffix: A pre-calculated number is added to the end of the partition value to enhance read performance.

29. Name the different consistency models in Cosmos DB.

Consistency levels or consistency models offer developers a selection process between high availability and better performance. The consistency models in Cosmos DB are as follows:

• Strong
• Bounded Staleness
• Session
• Consistent Prefix
• Eventual

30. How does data security get implemented in ADLS Gen2?

ADLS Gen2 comes with a multi-layered security model.  ADLS Gen2 has the following data security layers:

• Authentication
• Access Control
• Network Isolation
• Data Protection
• Advanced Threat Protection
• Auditing

31. What are the activities and pipelines in Azure?

A pipeline combines activities that are settled to accomplish a task. It lets users handle the individual activities as one group and offers a quick overview of the involved activities in a challenging task with multiple steps.

The grouping of ADF activities is in three parts:

• Data Transformation Activities
• Data Movement Activities
• Control Activities

32. How can you execute the data factory pipeline manually?

A pipeline can run with on-demand execution or manually.

To execute the pipeline programmatically or manually, you must implement the PowerShell command:

Invoke-AzDataFactoryV2Pipeline -DataFactory $df -PipelineName

"DemoPipeline" -ParameterFile .\PipelineParameters.json

The word ‘DemoPipeline’ refers to the pipeline that will function, and ‘ParameterFile’ represents the path of the JSON file containing the sink path and source.

Moreover, the JSON file’s format is passed as a parameter to the PowerShell command mentioned above:

{

  "sourceBlobContainer": "MySourceFolder,"

  "sinkBlobContainer": "MySinkFolder"

}

33. What is the difference between Control Flow and Data Flow in Azure Data Factory?

Control flow activity affects the execution path of the data factory pipeline. 

Data flow transformations are utilized when you are required to transform the input data.

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34. What is a data flow partitioning scheme?

A partitioning scheme optimizes data flow performance. This setting is accessible on the optimize tab of the configuration panel for the data flow activity.

35. Mention the data flow partitioning schemes in Azure.

The data flow partitioning schemes in Azure are as follows:

• Round Robin
• Hash
• Dynamic Range
• Fixed Range
• Key

36. Explain trigger execution in Azure data factory.

Pipelines can be automated or triggered in the Azure data factory.  Certain ways to trigger or automate the Azure data factory pipeline execution are as follows:

• Schedule Trigger
• Tumbling Window Trigger
• Event-based Trigger

37. Define mapping Dataflows.

Microsoft offers mapping data flows that don’t require writing code 4, a straightforward beta integration experience compared to data factory pipelines. This is a visual design way for data transformation flows. The data flow turns into Azure data factory activities, and execution occurs as part of the ADF pipelines.

38. What is the purpose of Azure data factory?

Azure data factory serves the following purposes:

• Data comes in multiple forms from different sources, and these sources channel old transfer data in multiple ways in different formats. When this data is brought to the cloud or specific storage, it must be managed well. You must ensure that the data is collected from multiple sources, brought to a common place and transformed into more meaningful data.
• Data factory assists in orchestrating the entire process in a more organized or manageable manner.

39. Define data modeling.

Data modeling includes crafting visual representations of the complete information system or its parts to represent links between structures and data points. The aim is to present the different types of data stored and used in the system, how data is classified and arranged, their relationship, and their features and formats. Data is modeled according to the requirements at multiple levels of abstraction. The process starts with stakeholders and the users offering information regarding business needs. Later, these business rules are transformed into data structures to build a concrete database design.

In data modeling, two types of design schemas are available:

• Star schema
• Snowflake schema

40. Mention the differences between the Star and Snowflake Schema.

To learn the difference between Star and Snowflake schema, refer to the table given below:

41. Name and explain the important concepts of the Azure data factory.

The important concepts of the Azure data factory are:

• Activities: It displays the pipeline’s processing steps. A pipeline includes one or multiple activities.
• Pipeline: It exists as a carrier in multiple processes occurring. 
• Linked services: It stores crucial information while connecting to any external source.
• Datasets: It is the data source or a data structure holding the data.

42. Mention the differences between HDInsight and Azure Data Lake Analytics.

43. Define Azure Synapse Runtime.

Azure Synapse uses runtimes to combine crucial component versions, Azure Synapse packages, optimizations, and connectors with a certain Apache Spark version. These run times upgrade periodically to involve new features, improvements, and patches.

These runtimes come with the following benefits:

• Faster times for session start-up
• Tested and assured compatibility with certain Apache Spark versions.
• Access to compatible, popular connectors and open-source packages.

44. Name and explain the different kinds of integration runtime.

The different kinds of integration runtime are as follows:

• Self-Hosted Integration Runtime: This software has code equal to Azure integration runtime. However, you must install it on a virtual machine or on-premise instrument in a virtual network. This self-hosted IR operates copy exercises between a public cloud data store and a private network.
• Azure Integration Runtime: It copies data among cloud data repositories and expresses the exercise to a computing service such as Azure HDInsight or SQL Server, where transformation happens.
• Azure SSIS Integration Runtime: This IR allows users to natively perform SQL Server Integration Services packages in a controlled environment. Hence, when users elevate the SSIS packages to the data factory, they work with Azure SSIS IR.

45. What are the common applications of Blob storage?

Some common applications of Blob storage are as follows:

• Laboring documents on images directly to a browser.
• Saving data for analysis by Azure-hosted or on-premises.
• Saving files for shared access.
• Streaming video and audio.
• Collecting data for archiving and recovery and backup disaster restoration.

46. Mention the major characteristics of Hadoop.

Some major characteristics of Hadoop are as follows:

• It cooperates with multiple hardware types and easily accesses distinct hardware within a specific node.
• Hadoop is an open-source structure that is ready for freeware.
• Hadoop promotes rapidly distributed data processing.
• It supports creating replicas for each block with varying nodes.

47. Define the Star scheme.

Star scheme is a highly manageable kind of data warehouse schema. It is called so due to its star-like construction. In a star schema, the star's heart might have many connected dimension tables and one particular table. This schema is practiced to question huge data sets.

48. How do you approve transferring data from one dataset to another?

Data efficiency alone, guaranteeing that no data is released, must be extremely important for any data engineer. Hiring administrators ask this question to acknowledge your thoughts on how data validation will occur. You must discuss proper validation and representations in multiple situations.

For example, You must recommend that validation can be a simple comparison or might occur after comprehensive data migration.

"The cloud isn’t just the future, it’s the present. Every digital transformation depends on it."
— Satya Nadella, CEO of Microsoft

49. Differentiate between unstructured and structured data.

50. Explain the data pipeline.

A data pipeline is a system that transports data from one source to another, such as a data warehouse. Along the journey, data is optimized and converted and eventually reaches a level that can be evaluated and used to produce business insights. Data pipelines are the processes involved in organizing, aggregating and transporting data. Several manual tasks are required to improve and process continuous data loads, but modern data pipelines can automate these tasks.

Key Skills & Qualifications Required for Azure Data Engineer

Some major data engineering skills and qualifications that are required to become an Azure data engineer are as follows:

Programming and Scripting Languages

Gaining proficiency in computer languages such as SQL for database querying and Python for data manipulation enables data analysis and processing efficiency.

Each language has unique advantages and uses in Azure data engineering. Personalize your mastery and learning based on the Azure services you will use and other project requirements.

Database Administration Skills

You must gain proficiency in handling Azure databases, including backup and recovery, performance tuning, routine maintenance, and ensuring efficient database reliability and functionality.

Cloud Platform Skills

Azure data engineers must have a strong hold on Microsoft Azure and cover multiple services for efficient deployment, scaling and data solution management by integrating the power of the cloud.

Data Integration and ETL Tools

As an Azure Data Engineer, you must master ETL tools and data integration, which are essential for smooth data processing. Azure data factory orchestrates data workflows.

Azure Synapse Analytics offers detailed ETL capabilities with its SQL-based ETL processes, while Azure Stream Analytics runs real-time data. These tools let you cleanse, ingest, transform and load data into specific destinations, promoting effective data integration and streamlined ETL processes to build strong data solutions on the Azure cloud.

Data Pipeline Development

It includes the skills to design and structure end-to-end data pipelines, seamlessly transferring and processing data from multiple sources to analytical and storage destinations, enabling valuable insights.

Apart from the skills discussed above, an Azure data engineer must gain the Azure Data Engineer certification by clearing the DP-203 exams. To become a certified Azure data engineer, individuals must clear these exams by being proficient in SQL syntax and programming concepts. Moreover, you must also have experience with data pipelines, distributed systems, and related database concepts.

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How Do I Prepare for the Azure Data Engineer Interview?

To prepare for the Azure data engineer interview:

• Learn about Azure data management services. Study Azure data lake, Azure data factory, Cosmos DB and Azure Stream Analytics in-depth.
• Understanding big data concepts, including ETL processes and data warehousing.
• Gain proficiency in relational databases, SQL, NoSQL, and My SQL. 
• Become capable of demonstrating problem-solving skills and discuss your approach towards real-world data engineering scenarios.
• Gain and elaborate on your experience building data pipelines and learn from the Azure data engineer interview questions mentioned above.

Career Growth Opportunities for Azure Data Engineer

Some major career growth opportunities for Azure data engineers are as follows:

• Azure Data Architect
• Azure Big Data Engineer
• Azure Machine Learning Engineer
• Azure Data Scientist
• Azure Data Governance Specialist
• Azure Data Warehouse Developer
• Azure Data Platform Consultant
• Azure Data Migration Specialist 
• Azure Data Warehouse Architect 
• Azure Data Engineer

Conclusion

Becoming an Azure data engineer includes extensive preparation on multiple aspects of Azure. Be sure to prepare well along with the Azure data engineer interview questions mentioned above, gain the required experience, master the needed skills and achieve the desired certification to become an expert Azure data engineer.

Mastering an Azure data engineer interview is not an easy task. Still, with Simplilearn’s Microsoft Certified Azure Data Engineer Associate: DP 203 course, you can outshine the competitive job market by preparing well and clearing your interview seamlessly.

FAQs

1. How to prepare for an Azure data engineer job?

To effectively prepare for an Azure data engineer job, look for and get certifications covering Azure tools and services, including Azure Databricks, Azure Data Factory, and Azure Synapse Analytics.

2. What is the role of Azure Data Engineer?

As an Azure data engineer, you are responsible for expanding and optimizing pipeline and data architectures and optimizing data collection and flow across different functional teams. Moreover, you must transform, integrate and consolidate data from multiple sources into structures for building analytics solutions.

3. What is the salary of an Azure Data Engineer?

The average salary range of an Azure data engineer falls between 5L - 12.1 L per year.

4. What's best to discuss past failures or challenges in an Azure Data Engineer interview?

To discuss your past challenges or failures:

• Explain any data issue that you’ve encountered and explain how you resolved it
• Describe what methodical approach you implemented
• Highlight your collaboration with stakeholders to refine data requirements
• Mention the positive outcomes of your solution

5. How can I effectively showcase problem-solving skills in an Azure Data Engineer interview?

To represent your problem-solving skills and discuss past challenges, elaborate on any complex data issue you resolved and explain how you did it.