30+ Azure Data Engineer Interview Questions

If you want to become a data engineer, you should prepare for the interview process. To assist you, we’ve compiled a list of the most important interview questions for this position. To help you get a head start on your preparation, I’ve compiled a list of the Top 30+ Azure Data Engineer Interview Questions.

Microsoft Azure is one of the most popular and rapidly expanding cloud service providers. Azure is expected to grow in the future, necessitating the hiring of more Azure professionals. When it comes to professionals, data engineers are the most in-demand in the IT industry. Most students are already preparing to become skilled data engineers, and we are here to cover some of the most frequently asked topics in Azure Data Engineering Interview Questions.

Interview Questions for Azure Data Engineer – General

1) What is the definition of Microsoft Azure?

Microsoft Azure is a cloud computing platform that includes hardware as well as software. In this case, the service provider creates a managed service that allows users to access these services on demand.

2) What is Azure’s primary ETL service?

Ingest	Control Flow	Data Flow	Schedule	Monitor
Multi-cloud and on-prem hybrid copy data	Design code-free data pipelines	Code-free data transform-ations that execute in Spark	Build and maintain operational schedules for your data pipelines	View active executions and pipeline history
90+ native connectors	Generate pipeline via SDK	Scale-out with Azure Integration Runtimes	Wall clock, event-based tumbling, windows, chained	Detail activity and data flow executions
Serverless and auto-scale	Utilize workflow construct loops,branches, conditional execution, variables, parameters…	Generate data flows via SDK		Establish alerts and notifications
Use wizard for quick copy jobs		Designers for data engineers and data analytics

3) What data masking options are available in Azure?

Dynamic data masking serves several important functions in data security. It restricts sensitive information to a small group of users.

• It is possible to use Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics.
• It can be set up as a security policy on all SQL Databases in an Azure subscription.
• Users can change the level of masking to suit their needs.
• It only masks the query results for specific column values that have data masking applied to them. It has no effect on the actual data stored in the database.

Also Read : What is integration runtime in Azure data factory?

4) What is Polybase?

Data entry into PDW is optimized by Polybase, which also supports T-SQL. It allows developers to query external data from supported data stores transparently, regardless of the storage architecture of the external data store.

One can use polybase:

• From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source.
• Use a few straightforward T-SQL queries to import data from Hadoop, Azure Blob Storage, or Azure Data Lake Store without having to install a third-party ETL tool.
• Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop. Data export and archiving to outside data stores are supported.

5) What does Azure’s reserved capacity mean?

To reduce the cost of Azure Storage, Microsoft offers the option of reserved capacity. The reserved storage on Azure cloud offers customers a set amount of capacity during the reservation period. Gen 2 data can be stored in a standard storage account for Block Blobs and Azure Data Lake.

Also Read How to create a pipeline in Azure data factory

Azure Synapse Interview Questions – Analytics

The interview questions and responses for azure data engineers for synapse analytics and stream analytics are covered in this section.

6) Which Azure service would you use to build a data warehouse?

Azure Synapse is a boundless analytics service that combines enterprise data warehousing and Big Data analytics. Users are given the choice to query data on specific terms for using either serverless on-demand or scale-out provisioned resources.

7) Describe the Azure Synapse Analytics architecture

It is intended to process enormous amounts of data, including tables with hundreds of millions of rows. Due to Synapse SQL’s Massively Parallel Processing (MPP) architecture, which distributes data processing across multiple nodes, complex queries are processed by Azure Synapse Analytics, which returns the query results in a matter of seconds even when there is a large amount of data.

Applications communicate with a control node that serves as the gateway to the Synapse Analytics MPP engine. The control node converts the Synapse SQL query into MPP-optimized format after receiving it. Additionally, the individual operations are sent to the compute nodes so they can be completed in parallel, greatly improving query performance.

Also Read :What is Azure Cosmos DB

8) Difference between ADLS and Azure Synapse Analytics

Fig: Image by Microsoft

Highly scalable and capable of ingesting and processing enormous amounts of data, Azure Data Lake Storage Gen2 and Azure Synapse Analytics are both available (on a Peta Byte scale). However, there are some distinctions.

9) What are Dedicated SQL Pools?

Fig:   From Microsoft

A group of features known as Dedicated SQL Pool make it possible to use Azure Synapse Analytics to implement the more conventional Enterprise Data Warehousing platform. Data Warehousing Units (DWUs), which are provisioned using Synapse SQL, are used to measure the resources. A dedicated SQL pool stores data using relational tables and columnar storage, which enhances query performance and lowers the necessary amount of storage.

10) How do you capture streaming data in Azure?

Azure offers a specialized analytics service called Azure Stream Analytics, which offers the straightforward SQL-based Stream Analytics Query Language. By defining additional ML (Machine Learning) functions, it enables developers to expand the capabilities of the query language. Over a million events per second can be processed by Azure Stream Analytics, and the results can be delivered with extremely low latency.

Also Read : Azure Databricks Architecture Overview

11) What are the various windowing functions in Azure Stream Analytics?

A block of time-stamped event data known as a window in Azure Stream Analytics allows users to run different statistical operations on the event data.

To divide and analyse a window in Azure Stream Analytics, there are four different types of windowing functions available:

1. Tumbling Window: The tumbling window function divides the data stream into distinct, fixed-length time segments.
2. Hopping Window: The data segments in hopping windows may overlap.
3. Sliding Window: Aggregation happens every time a new event happens, in contrast to Tumbling and Hopping windows.
4. Session window: Timeout, maximum duration, and partitioning key are the three parameters, which has no set size. This window’s goal is to eliminate periods of silence in the data stream.

Also Read : What is Delta Lake?

Azure Data Engineering Interview Questions – Storage

This section includes azure data engineering interview questions and solutions pertaining to databases and storage.

12) What are the various Azure storage options?

Azure offers five different types of storage:

1. Azure Blobs: A large binary object is referred to as a “blob.” All types of files, including text files, videos, images, documents, binary data, etc., can be supported by it.
2. A cloud-based messaging store called Azure Queues is used to establish and facilitate communication between various applications and components.
3. An organised method of storing data in the cloud is Azure Files. The main advantage of Azure Files over Azure Blobs is that it allows for folder-based data organisation and is SMB compliant, allowing for use as a file share.
4. Storage for Azure VMs is provided by Azure Disks (Virtual Machines).
5. For storing structured data that does not adhere to the typical relational database schema, use Azure Tables, a NoSQL storage solution.

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13) Examine the capabilities of Azure storage explorer.

It is a flexible standalone application that can manage Azure Storage from any platform and is available for Windows, Mac OS, and Linux. Microsoft offers a download for Azure Storage.

It offers simple GUI access to a variety of Azure data stores, including Blobs, Queues, Tables, ADLS Gen2, Cosmos DB, and more.

By attaching local emulators, one of the key features of Azure Storage Explorer is that it enables users to continue working even when they are not connected to the Azure cloud service.

14) What are Azure Databricks, and how are they unique from standard data bricks?

An open-source big data processing platform is Apache Spark in its Azure version. Azure Databricks is a component of the data preparation or processing phase of the data lifecycle. Data is initially imported into Azure through Data Factory and kept in permanent storage (such as ADLS Gen2 or Blob Storage). Additionally, Databricks processes data using Machine Learning (ML), and the insights that are gleaned are then loaded into Azure Analysis Services like Azure Synapse Analytics or Cosmos DB.

Finally, with the aid of analytical reporting tools like Power BI, insights are visualised and presented to the end users.

15) What is Azure table storage, exactly?

It’s a storage service that is designed to store structured data efficiently. The basic units of structured data, which correspond to the rows in a relational database table, are called table entities. Table entities each represent a key-value pair and have the following characteristics:

• PartitionKey: It keeps the partition key that the table entity belongs to.
• RowKey: This identifies the entity within the partition in a special way.
• TimeStamp: It maintains the table entity’s last modified date/time value.

16) In Azure, what is serverless database computing?

Program code typically resides either on the client-side or the server in a computing environment. However, serverless computing adheres to the stateless nature of code, which means that the code does not need any infrastructure.

Users are required to pay for the compute resources that the code uses while being executed for a brief time. Users only have to pay for the resources they actually use, which makes it extremely cost-effective.

17) What Azure SQL DB data security options are offered?

The following are the data security choices offered by Azure SQL DB:

1. Rules for the Azure SQL Firewall: Azure offers two levels of security. The first is server-level firewall rules, which control access to the Azure database server and are kept in the SQL Master database. The second is firewall rules at the database level that control access to each individual database.
2. Azure SQL Always Encrypted: This feature is intended to safeguard private information kept in the Azure SQL database, such as credit card numbers.
3. Data encryption technology used in the Azure SQL Database is called Transparent Data Encryption (TDE). TDE is used for real-time database encryption and decryption as well as log file backups and transactions.
4. Azure SQL Database Auditing: Azure offers SQL Database service auditing capabilities. At the database server or individual database level, the audit policy can be specified.

18) What does Azure data redundancy mean?

To ensure high levels of data availability, Azure continuously keeps multiple copies of the data. Based on the urgency and time required to grant access to the replica, some data redundancy solutions are available to clients in Azure.

Data is replicated across various racks in the same data centre with locally redundant storage (LRS). It ensures that there are at least three copies of the data and is the least expensive redundancy option.

Data replication across three zones within the main region is ensured by zone redundant storage (ZRS). When a zone fails, Azure handles DNS repointing automatically. Any applications that access data after DNS repointing may need to make a few adjustments to the network settings.

Geo-Redundant Storage (GRS): This type ensures that data can be recovered even if one entire region goes down by replicating data across two regions. The completion of the Geo failover and the availability of data in the secondary region may take some time.

RA-GRS: Read Access Geo Redundant Storage is very comparable to GRS but adds the capability to read access to data in the secondary region in the event of a primary region failure.

19) How can data be transferred from on-premises storage to Azure?

The following are the main things to think about when selecting a data transfer solution:

• Data Size
• Data Transfer Frequency (One-time or Periodic)
• Internet bandwidth

The following data movement solutions are possible based on the aforementioned variables:

Offline data transfer: This is done in bulk once. Thus, Microsoft can offer discs or secure storage devices to customers, or customers can send Microsoft their own discs. Data Box, Data Box Disk, Data Box Heavy, and Import/Export (Customer’s Own Disks) are the offline transfer options.

Network transfer: The following methods for performing data transfer over a network connection:

When only a few files need to be transferred and no automation is required, a graphical interface is the best option. Azure Storage Explorer and Azure Portal are graphical interface choices.

Programmatic Transfer: AzCopy, Azure PowerShell, and Azure CLI are a few scriptable data transfer tools that are readily available. There are also many different SDKs for programming languages.

On-site equipment At the customer’s location, a physical device called Data Box Edge and a virtual Data Box Gateway are installed to optimize the data transfer to Azure.

Managed Data Factory pipeline: Azure Data Factory pipelines are able to automate routine data transfers from on-premises data stores to Azure and move, transform, and move data.

20) How can data be moved most effectively from an on-premises database to Azure?

Azure offers the following options for data migration from an existing on-premises SQL Server to an Azure SQL database:

Data is transferred from SQL Server 2016 to Azure using the SQL Server Stretch Database. In order to move those rows to the cloud, it can identify the cold rows that users access infrequently. As a result, the on-premises database’s backups are completed more quickly.

Azure SQL Database: It is appropriate for businesses that want to move their entire database to Azure as part of a cloud-only strategy.

Azure Database as a Service configurations are supported by SQL Server Managed Instance (DBaaS). The database’s upkeep is handled by Microsoft, and it is almost entirely compatible with SQL Server installed on-site.

On a virtual machine, SQL Server: It is an appropriate choice for a customer who desires total control over database management and upkeep. It makes sure that the current on-premises instance is completely compatible.

Additionally, Microsoft offers a tool called Data Migration Assistant that can assist users in finding appropriate options based on their current on-premises SQL Server configuration.

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21) What are databases with multiple models?

Microsoft’s top NoSQL service on Azure is Azure Cosmos DB. It is the first globally distributed, multi-model database that any vendor is making available in the cloud. It is employed to store data in a number of different data storage models, including key-value pair, document-based, graph-based, column-family-based, etc. Regardless of the data model the customer chooses, features like low latency, consistency, global distribution, and automatic indexing remain the same.

22) What does the artificial partition key for Azure Cosmos DB mean?

It is essential to choose a solid partition key that can evenly distribute the data across several partitions. When there is no right column with evenly distributed values, we can create a synthetic partition key. The three methods for producing a fake partition key are as follows:

Concatenate Properties: Concatenate several property values to create a fake partition key.

Random Suffix: The partition key value is finished off with a random number.

To enhance the read performance, a pre-calculated suffix is added to the end of the partition value.

23) What different Cosmos DB consistency models are there?

Developers have a choice between better performance and high availability thanks to consistency models or consistency levels.

The following consistency models are offered by Cosmos DB:

1. Strong: Every time a read operation takes place, it fetches the most recent version of the data. Compared to other consistency models in this model, the cost of the reading operation is higher.
2. Bounded Staleness: It enables adjusting the amount of time that must pass between writing and reading. It is appropriate for situations where consistency and availability are equally important.
3. Session: Based on regions, it is the default and most used consistency level in Cosmos DB. When a user accesses the same region where the write was made, they will see the most recent data. It provides the read and write operations with the lowest latency among all consistency levels.
4. Consistent Prefix: It ensures that users do not observe out-of-order writes, but there is no time-bound data replication across regions.
5. Actual: It does not ensure replication that is time- or version-bound. It is the most consistent and has the smallest read latency.

24) How is ADLS Gen2 data security implemented?

A multi-layered security model is used by ADLS Gen2. The ADLS Gen2 data security layers are as follows:

1. Three authentication methods—Azure Active Directory (AAD), Shared Key, and Shared Access Token—are used to secure user accounts (SAS).
2. Access Control: Using roles and access control lists, it limits access to specific containers or files (ACLs).
3. Network Isolation: It gives administrators the ability to enable or disable access to particular IP addresses or Virtual Private Networks (VPNs).
4. Encrypts data while it is in transit using HTTPS for data security.
5. Advanced Threat Protection: This feature allows for the monitoring of unauthorised attempts to use the storage account or gain access.

The last layer of security is auditing, and ADLS Gen2 offers thorough auditing features that log all account management activity.

Azure Data Engineering Interview Questions – Azure Data Factory

The interview questions for Azure Data Engineer for Azure Data Factory are covered in this section (ADF).

25) In Azure, what are pipelines and activities?

Pipelines are the arrangement of activities designed to complete a task simultaneously. Users can manage individual tasks as a single group with its help, and it offers a quick overview of all the steps in a multi-step, complex task.

ADF operations are divided into three categories:

• Activities for data movement are used to import data into Azure or export data out of Azure to other data stores.
• Data processing and information extraction-related activities are referred to as data transformation activities.
• Control Activities: Set a condition or impede the pipeline’s progress.

26) How is the Data Factory pipeline manually run?

A pipeline may be executed manually or on demand.

We can use the PowerShell command to manually or automatically run the pipeline:

Invoke-AzDataFactoryV2Pipeline -DataFactory $df -PipelineName

"DemoPipeline" -ParameterFile .PipelineParameters.json

The pipeline's running name is "DemoPipeline," and the "ParameterFile" specifies the location of a JSON file containing the source and sink paths.

In addition, the following JSON file format must be supplied as a parameter to the PowerShell command mentioned above:

[java]

{

"sinkBlobContainer": "MySourceFolder,"

"sinkBlobContainer": "MySourceFolder,"

}

27) Control Flow vs. Data Flow in Azure Data Factory

An activity called Control Flow has an impact on the Data Factory pipeline’s execution path. For instance, a process that starts a loop if certain criteria are met

When we need to transform the input data, such as with a join or conditional split, we use data flow transformations.

The following are some distinctions between Data Flow Transformations and Control Flow Activities:

28) List the Azure data flow partitioning strategies

A partitioning scheme can improve the efficiency of data flow. The Optimize tab of the configuration panel for the Data Flow Activity contains a link to the partitioning scheme setting.

In most situations where native partitioning schemes are used, Microsoft recommends using the default setting of “Use current partitioning.”

When users want to output to a single destination, such as a single file in ADLS Gen2, they use the “Single Partition” option.

Several partitioning plans include:

• Round Robin: A straightforward partitioning strategy that evenly distributes data among partitions
• Hash: A hash of the columns used to make uniform partitions (similar values in a partition)
• Dynamic Range: Spark dynamic range based on specified columns or expressions
• Fixed Range: Partition for a fixed range of values based on user-supplied expressions
• Key: Create a partition for each distinct value.

29) How does Azure Data Factory execute triggers?

Pipelines in Azure Data Factory can be automated or triggered.

The following are some techniques for automating or starting Azure Data Factory Pipelines:

• Schedule Trigger: It starts the pipeline at a specific time or according to a predetermined schedule, such as weekly, monthly, etc.
• Tumbling Window Trigger: It starts the Azure Data Factory Pipeline at a specified time and runs it at predetermined fixed periodic intervals without overlapping.
• Event-Based Trigger: It starts an Azure Data Factory Pipeline when a certain event occurs, like when a new file is added to or removed from Azure Blob Storage.

30) What are dataflow mappings?

For a simpler data integration experience than Data Factory Pipelines, Microsoft offers Mapping Data Flows that doesn’t require writing any code. It is a method of creating data transformation flows visually. The data flow is transformed into Azure Data Factory (ADF) tasks and carried out as a component of ADF pipelines.

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Conclusion

The most popular cloud platform is Azure, and businesses are constantly looking for qualified personnel. We worked hard to compile a list of popular subjects for Azure Data Engineer Interview Questions in order to aid you in landing a job.

If you want to get trained in Azure data engineer certification, then check out the Microsoft Azure Data Engineering Certification Course (DP-203) by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe.

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