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For those, who have already completed Microsoft Azure Data Engineer [DP-203] Training & Certification Course and looking forward to prepare for Azure Data Engineer interview, here is a list of top 20 commonly-asked Azure Date Engineer interview questions and answers.
The process of filtering, cleaning, profiling and transforming huge data is called data engineering. In a nutshell, data engineering refers to collection of data and analysis. Data that is collected in raw form is transformed into useful information with the help of data engineering.
2. What is Azure Synapse analytics?
Azure Synapse Analytics is a limitless analytics service that allows you users to query data at their own terms by bringing together big data analytics, enterprise data warehousing and data integration. It offers unified experience in ingesting, exploring, preparing, transforming, managing and serving data for immediate machine learning and BI needs.
3. Define Azure data masking feature?
Data masking feature of Azure enables to avert unauthorized access to sensitive data. With the help of this policy-based security feature, customers can decide how much of the sensitive data they wish to reveal without putting much impact on the application layer. The Dynamic data masking features masks data from non-privileged users by limiting acute data exposure and hiding sensitive data in a query result set over the designated data fields.
Azure Data Masking policies allow you to define rules that determine how sensitive data is masked. Azure Data Masking policies provide an additional layer of security to help you protect sensitive data in your Azure SQL Database or Azure Synapse Analytics instance.
4. What is the difference between Azure Synapse Analytics and Azure Data Lake Storage?
Azure Synapse Analytics and Azure Data Lake Storage are two related but distinct services offered by Microsoft Azure. Azure Synapse Analytics is an analytics service that provides end-to-end analytics solutions for large-scale data processing, data warehousing, and big data analytics while Azure Data Lake Storage is a cloud-based data storage solution designed for big data analytics workloads. Although both services are designed to handle big data analytics workloads, they serve different purposes and can be used together to create powerful big data solutions in the Azure cloud.
5. What are the various storage types in Azure?
There are 5 storage types in Azure: Files, Blobs, Queues, Disks and Tables.
Here is what these terms mean:
Files: It is an Azure File Storage service that allows users to store data on the cloud. When compared to Azure Blobs, Azure files allow users to organize data in folder structure. They are also Server Message Block (SMB) protocol compliant that means Azure Files can be used as file share.
Blobs: The term BLOB stands for Binary Large Objects. It is Microsoft’s object storage solution for Cloud that allows storing large quantities of unstructured data such as multimedia files and images on the Microsoft’s data storage platform.
Queues: It is a service used to store large amount of messages that can be accessed from any corner of the world through authenticated calls using HTTPS or HTTP.
Disks: They are durable and high-performance block-storage that are used with Azure VMware Solution and Azure Virtual Machines and managed by Azure.
Tables: They store structured NoSQL data or non-relational structured data in cloud, providing schema less design.
6. What are the different security options available in the Azure SQL database?
Azure SQL Database provides various security options to help you protect your data and meet your compliance requirements. Some of the key security options available in Azure SQL Database are:
Azure Active Directory authentication: You can use Azure AD authentication to authenticate users and applications with Azure SQL Database. Azure AD authentication allows you to use the same credentials for accessing Azure SQL Database and other Azure services.
Transparent Data Encryption (TDE): TDE helps protect data at rest by encrypting the entire database, including the system database and user database files. It’s a default enabled in Azure SQL Database.
Always Encrypted: Always Encrypted is a feature that helps protect sensitive data such as credit card numbers, social security numbers, and passwords. Two keys type are used by Always Encrypted: Column Encryption Key and Column Master Key. With Always Encrypted, data is encrypted at rest and in transit.
Row-Level Security (RLS): RLS allows you to restrict data access based on a user’s role or access level. With RLS, you can control which users can view, modify or delete data in the database.
Azure Virtual Network (VNet) service endpoints: You can use Azure VNet service endpoints to help secure your database by restricting access to a specific VNet or IP address range.
Auditing and Threat Detection: Azure SQL Database includes auditing and threat detection capabilities to help you monitor database activity and detect suspicious activity. Auditing can be configured to capture events such as logins, schema changes, and data access. Threat detection can alert you to potential security threats, such as SQL injection attacks or anomalous login patterns.
Firewall: Azure SQL Database also includes firewall capabilities that allow you to restrict access to your database based on IP address ranges. The firewall settings can be configured at the server level or at the database level.
7. Explain data security implementation in Azure Data Lake Storage (ADLS) Gen2.
Azure Data Lake Storage Gen2 (ADLS Gen2) provides various security features to help you secure your data in the cloud. Here are some of the key data security implementation features in ADLS Gen2:
Role-Based Access Control (RBAC): ADLS Gen2 uses Azure RBAC to manage access to data. RBAC allows you to control who can access the data and what they can do with it. You can assign roles such as owner, contributor, or reader to users, groups, or service principals and these roles determine the level of access to the data.
Azure Active Directory (AAD) integration: ADLS Gen2 integrates with Azure AD, which allows you to manage access to the data using AAD identities. This integration provides a central location for managing access to the data, and you can use Azure AD authentication to control access to the data.
Virtual Networks (VNet) service endpoints: ADLS Gen2 allows you to create VNet service endpoints, which provide a secure connection between your virtual network and your data in ADLS Gen2. By using VNet service endpoints, you can restrict access to your data to only the resources that are within the same virtual network.
Encryption: ADLS Gen2 encrypts data at rest using Azure Storage Service Encryption (SSE), which provides automatic encryption of data in the storage account. Additionally, you can enable customer-managed keys (CMK) to encrypt your data with keys that you manage in Azure Key Vault.
Data protection: ADLS Gen2 provides Data Lake Storage File System (ADLSFS) access control lists (ACLs) to help you protect your data. ACLs allow you to define permissions for files and directories in the data lake, and you can control who can read, write, and execute data.
Firewall: ADLS Gen2 includes firewall capabilities that allow you to restrict access to your data based on IP address ranges. The firewall settings can be configured at the storage account level or at the container level.
Monitoring and Auditing: ADLS Gen2 provides logging and auditing capabilities to help you monitor your data. You can use Azure Monitor to track activities and events in ADLS Gen2, and you can also use Azure Log Analytics to collect and analyze data about access to your data.
8. What is Azure Data Factory and why is it needed?
Azure Data Factory is a cloud-based data integration and ETL service for Azure. This cloud-based data integration service allows you to create, schedule, and manage data pipelines. Here are some of the benefits of Azure Data Factory:
Provides a user-friendly interface that enables you to create data pipelines using a drag-and-drop approach. Enables building data integration workflows with ease without the need of extensive coding knowledge.
Supports hybrid data integration that enables you to connect on-premises data sources with cloud-based data stores. You can use Azure Data Factory to move data from on-premises to the cloud or vice versa.
Offers fully managed service that scales on-demand to meet your data integration needs. One can use Azure Data Factory to handle data pipelines of any size and complexity.
Integrates with various Azure services such as Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Blob Storage, allowing you to build end-to-end data processing workflows.
Provides several security features to help protect your data, including support for Azure Active Directory and encryption at rest and in transit.
Provides comprehensive monitoring and management features that enable you to track data pipeline activity, identify issues, and troubleshoot problems. You can use Azure Monitor to monitor pipeline activity and view metrics, logs, and alerts.
Follows a pay-as-you-go pricing model, which means you only pay for the data pipelines you create and the resources you use. This makes it a cost-effective solution for building data integration workflows.
9. What is Azure Synapse Runtime?
The Azure Synapse Runtime is a powerful analytics engine that provides an optimized and scalable environment for big data processing. It integrates with other Azure services and provides built-in security features, making it a comprehensive solution for building end-to-end analytics solutions.
10. What is SerDe in HIVE?
SerDe is a key component of Apache Hive that provides the ability to read and write data in different formats. It enables Hive to work with various data formats and allows data to be easily inserted and queried from Hive tables.
Hive tables can be created with a specified SerDe, which enables Hive to understand the format of the data stored in the table. When data is inserted into or read from the table, the SerDe is used to convert the data to or from the table’s internal format.
11. What is Star scheme?
The Star schema is a data modeling technique used in data warehousing to organize data into a central fact table and a set of dimension tables. It provides a simple and intuitive way to organize large amounts of data and enables efficient querying of the fact data. The Star schema is named for its visual representation, which resembles a star with the fact table at the center and the dimension tables radiating out from it.
Some of the benefits of the Star schema include:
Simplicity and ease of use
Fast query performance
Supports complex queries and reporting
Ability to handle data in massive quantities
Provides clear data relationships and hierarchies
12. What are the key components of Azure Data Factory?
13. Explain the main difference between IaaS and PaaS
IaaS (Infrastructure as a Service) products allow companies to manage business resources, including servers, network, and data storage on cloud. PaaS (Platform as a Service) products allow developers and businesses build, host and deploy consumer-facing apps. In short, IaaS allows users to access resources such as virtual storage and virtual machines; while PaaS provides deployment tools, execution environments and application development.
14. What is PolyBase?
PolyBase is a feature in SQL Server and Azure SQL Database that enables users to query and access data from external data sources, such as Hadoop and Azure Blob Storage, using standard SQL commands. It simplifies the process of accessing and analyzing data from multiple sources and provides integration with other Azure data services.
15. Define the steps to create the ETL process in Azure Data Factory.
Steps to create the ETL process in the Azure Data Factory are as follows:
Go to the SQL Server Database
For the source data store, create a Linked Service
Build a Linked Service i.e. the Azure Data Lake Store, for the destination data store.
Create a dataset for Data Saving purposes
Build the pipeline.
Add the copy activity.
Plan the pipeline by attaching a trigger.
16. What is the main difference between Azure Data Lake Analytics & HD insight?
HDInsight and Azure Data Lake Analytics are both cloud-based big data processing platforms that differ in architecture, processing capabilities, programming languages, data integration, and security and governance features. HDInsight is based on Apache Hadoop and provides a wide range of processing capabilities, while Azure Data Lake Analytics primarily uses U-SQL for batch processing and analytics.
17. Explain the differences between Azure Data Lake Analytics & HDinsight.
HDInsight and Azure Data Lake Analytics are both cloud-based big data processing platforms offered by Microsoft, but they differ in several ways.
Architecture: HDInsight is a fully-managed cloud service that is based on Apache Hadoop, which is an open-source framework for distributed storage and processing of large data sets. Azure Data Lake Analytics is a cloud-based analytics service that uses a distributed query engine called U-SQL to process and analyze large amounts of structured and unstructured data.
Processing capabilities: HDInsight provides a broad range of big data processing capabilities, including batch processing, stream processing, interactive querying, and machine learning. Azure Data Lake Analytics is focused primarily on batch processing and analytics, with support for data preparation, transformation, and analysis using U-SQL.
Programming languages: HDInsight supports a wide range of programming languages and frameworks, including Hadoop MapReduce, Hive, Pig, Spark, and more. Azure Data Lake Analytics primarily uses U-SQL, which is a SQL-like language that supports C# and R programming.
Data integration: HDInsight provides integration with a wide range of data sources and destinations, including Azure Blob Storage, Azure SQL Database, and Azure Data Lake Storage. Azure Data Lake Analytics is primarily designed to work with Azure Data Lake Storage and other Azure data services.
Security and governance: HDInsight provides robust security and governance features, including encryption, authentication, and authorization mechanisms, as well as compliance with regulatory standards such as HIPAA and SOC. Azure Data Lake Analytics provides similar security and governance features, with support for role-based access control and Azure fundamental Active Directory integration.
18. What is Azure DataBricks?
Azure Data bricks Lakehouse offers a set of tools that are used to build, deploy, share, and maintain enterprise-grade data solutions at scale. It integrates with security and cloud storage in user’s cloud account to deploy and manage cloud infrastructure on behalf of the user. It is a cloud-based data engineering tool that enables to process and transform massive amounts of data and explore the data via machine learning models.
19. How to schedule a pipeline?
One can schedule a pipeline with the help of time window trigger or scheduler trigger. This trigger features wall-clock calendar schedule, which is used to plan pipelines at calendar-based recurring patterns or periodic intervals.
20. To create data flows, which Data Factory version is required?
To create data flows, it is recommended to use the Data Factory V2 version.
These are some of the commonly asked Azure Date Engineer Interview Questions with answers. But, if you have time in hand and want to prepare for Azure data engineer interview, quickly enroll for Microsoft Azure Data Engineer [DP-203] Online Training & Certification Course from Multisoft Virtual Academy.
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Multisoft Virtual Academy has been in training industry for more than 2 decades and backed by a team of global subject matter experts from around the world. With Multisoft, you get the opportunity to learn from experienced industry experts and gain experience and skills with hands-on experience from projects and assignments based on real-life examples. You will avail perks like lifetime access to e-learning material, recorded training session videos and after training support.
Conclusion: Microsoft Azure Data Engineer [DP-203] Online Training & Certification Course from Multisoft Virtual Academy is beneficial for everyone, who wishes to start his/her career in data engineering. This course will not just help you develop skills in Azure Data Engineering, but also gain hands-on experience while preparing you for interview with lots of practice tests.