{"id":2963,"date":"2024-04-22T10:13:00","date_gmt":"2024-04-22T10:13:00","guid":{"rendered":"https:\/\/www.aegissofttech.com\/insights\/?p=2963"},"modified":"2026-03-18T10:33:25","modified_gmt":"2026-03-18T10:33:25","slug":"azure-data-factory-interview-questions","status":"publish","type":"post","link":"https:\/\/www.aegissofttech.com\/insights\/azure-data-factory-interview-questions\/","title":{"rendered":"Top 27+ Azure Data Factory Interview Questions"},"content":{"rendered":"<p><span dir=\"ltr\" lang=\"EN\">Azure Data Factory is a cloud-based Microsoft device that gathers crude business information and changes it into usable data. There is a significant interest in Azure Data Factory Engineers in the business. Hence, cracking its interview needs a bit of homework. This blog lists the most common Azure Data Factory Interview Questions asked during Data Engineers\u2019 job interviews.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Whether you\u2019re a beginner or an experienced professional, these questions will help you prepare for your data engineering job interview:<\/span><\/p>\n<h2><span dir=\"ltr\" lang=\"EN\"><strong>Azure Data Factory Interview Questions And Answers for Beginners<\/strong><\/span><\/h2>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>1. Define Azure Data Factory.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines. It enables data movement and transformation from various sources to destinations.<\/span><\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-2964 size-full\" src=\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/04\/do-data-engineering-pipelines-with-azure-data-factory-and-synapse.webp\" alt=\"Define Azure Data Factory\" width=\"680\" height=\"293\" title=\"\" srcset=\"https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/04\/do-data-engineering-pipelines-with-azure-data-factory-and-synapse.webp 680w, https:\/\/www.aegissofttech.com\/insights\/wp-content\/uploads\/2024\/04\/do-data-engineering-pipelines-with-azure-data-factory-and-synapse-300x129.webp 300w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: Integration Runtime (IR) is a computing infrastructure used by <\/span><a href=\"https:\/\/www.aegissofttech.com\/microsoft\/azure-data-factory-consulting\" target=\"_blank\" rel=\"noopener noreferrer\"><span dir=\"ltr\" lang=\"EN\">Azure Data Factory Developers<\/span><\/a><span dir=\"ltr\" lang=\"EN\"> to provide data integration capabilities across different network environments. It facilitates data movement between on-premises and cloud data stores.<\/span><\/p>\n<p>Read: <a href=\"https:\/\/www.aegissofttech.com\/insights\/datasets-in-azure-data-factory\/\">Datasets in Azure Data Factory: A Comprehensive Guide<\/a><\/p>\n<h3><strong>2. Explain what Integration Runtime is in Azure Data Factory<\/strong><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: <\/span>Integration Runtime (IR) is a compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments. It facilitates data movement between on-premises and cloud data stores.<\/p>\n<h3><strong>3. <span dir=\"ltr\" lang=\"EN\">Define what Self-Hosted Integration and Azure SSIS Integration Runtimes are.<\/span><\/strong><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Self-Hosted Integration Runtime: It allows data movement between on-premises data stores and Azure services. You install and manage it on your own infrastructure.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Azure SSIS Integration Runtime: It\u2019s a managed service for running SQL Server Integration Services (SSIS) packages in Azure. It provides scalability and flexibility for ETL processes.<\/span><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/learn.microsoft.com\/en-us\/azure\/data-factory\/media\/create-self-hosted-integration-runtime\/high-level-overview.png\" alt=\"Create a self-hosted integration runtime - Azure Data Factory &amp; Azure Synapse | Microsoft Learn\" title=\"\"><\/p>\n<h3><strong>4. Differentiate Azure Data Lake from Azure Data Warehouse <\/strong><\/h3>\n<p><strong><span dir=\"ltr\" lang=\"EN\">Answer:<\/span><\/strong><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Azure Data Lake: It\u2019s a large-scale data lake storage service for big data analytics. It stores unstructured and semi-structured data.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Azure Data Warehouse (Synapse Analytics): It\u2019s a fully managed <a href=\"https:\/\/www.aegissofttech.com\/data-warehouse-services\" target=\"_blank\" rel=\"noopener\">data warehouse service<\/a> for structured data. It\u2019s optimized for analytical workloads.<\/span><\/li>\n<\/ul>\n<picture><img decoding=\"async\" src=\"https:\/\/i.ytimg.com\/vi\/31ihSyZECH8\/maxresdefault.jpg\" alt=\"What is the difference between Azure Data Lake Store &amp; Azure SQL Data Warehouse? - YouTube\" title=\"\"><\/picture>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>5. Walk us through how you normally create an ETL process in Azure Data Factory.<\/strong><\/span><picture><source srcset=\"https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/80.webp 80w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/160.webp 160w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/240.webp 240w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/320.webp 320w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/400.webp 400w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/480.webp 480w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/560.webp 560w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/640.webp 640w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/720.webp 720w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/VZlHW1hZ7tD3\/images\/734.webp 734w\" type=\"image\/webp\" sizes=\"(max-width: 734px) 100vw, 734px\" \/><\/picture><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: <\/span><span dir=\"ltr\" lang=\"EN\">An ETL (Extract, Transform, Load) process in ADF involves:<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Extract: Retrieve data from various sources (files, databases, APIs).<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Transform: Apply data transformations (filtering, aggregations, joins).<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Load: Load the transformed data into target destinations (Azure SQL Database, Data Lake, etc.).<\/span><\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/www.mshowto.org\/images\/articles\/2022\/07\/image001.png\" alt=\"ETL as a Service: Azure Data Factory\" title=\"\"><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>6. What are the top-level concepts of Azure Data Factory?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: Key concepts include linked services, datasets, pipelines, activities, triggers, and integration runtimes.<\/span><\/p>\n<picture><source srcset=\"https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/Q2idzSXu-Rv3\/images\/80.webp 80w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/Q2idzSXu-Rv3\/images\/160.webp 160w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/Q2idzSXu-Rv3\/images\/240.webp 240w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/Q2idzSXu-Rv3\/images\/320.webp 320w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/Q2idzSXu-Rv3\/images\/339.webp 339w\" type=\"image\/webp\" sizes=\"(max-width: 339px) 100vw, 339px\" \/><\/picture> <picture><source srcset=\"https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/80.webp 80w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/160.webp 160w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/240.webp 240w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/320.webp 320w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/400.webp 400w,https:\/\/ckbox.cloud\/5134f02bf818f7c93ef3\/assets\/FL6S3je613Nj\/images\/426.webp 426w\" type=\"image\/webp\" sizes=\"(max-width: 426px) 100vw, 426px\" \/><\/picture><img decoding=\"async\" src=\"https:\/\/intellipaat.com\/blog\/wp-content\/uploads\/2019\/07\/AzureDataFactory2.jpg\" alt=\"Azure Data Factory Tutorial for Beginners\" title=\"\"><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>7. What are the security levels in ADLS Gen2?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: ADLS Gen2 (Azure Data Lake Storage Gen2) supports three security levels: Blob-level, File-level, and Directory-level.<\/span><\/p>\n<h2><span dir=\"ltr\" lang=\"EN\"><strong>Top Azure Data Factory Interview Questions 2024- Scenario-Based<\/strong><\/span><\/h2>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>8. You need to copy data from an on-premises SQL Server database to Azure SQL Database. How would you design this data pipeline in ADF?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: Use a pipeline with a source dataset pointing to the on-premises SQL Server and a sink dataset pointing to the Azure SQL Database. Configure the appropriate integration runtime for the on-premises connection.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>9. How would you handle incremental data loads in ADF?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: Use watermark-based logic or change tracking to identify new or modified records since the last load. Implement this logic in your data flow or pipeline.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>10. Differentiate between a Dataset and a Linked Service in Azure Data Factory.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer:<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Dataset: Represents the data structure within a data store (e.g., a table in a database or a file in a storage account).<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Linked Service: Defines the connection information to external data stores (e.g., Azure SQL Database, Azure Blob Storage).<\/span><\/li>\n<\/ul>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>11. How would you handle incremental data loads in Azure Data Factory?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: <\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">To handle incremental loads, use watermark-based logic or change tracking. Identify new or modified records since the last load and implement this logic in your data flow or pipeline.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>12. Explain the concept of partitioning in Azure Data Factory.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: <\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Partitioning involves dividing large datasets into smaller chunks (partitions) for parallel processing. ADF can partition data during data movement or transformation activities to improve performance.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>13. What are the benefits of using Azure Data Factory over traditional ETL tools?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer:<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Scalability: ADF scales automatically based on workload demands.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Serverless: No need to manage infrastructure.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Integration with Azure Services: Seamlessly integrates with other Azure services.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Cost-Effective: Pay only for what you use.<\/span><\/li>\n<\/ul>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>14. How would you handle schema changes in source data during ETL processes?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Answer: <\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Use dynamic mapping or schema drift handling in ADF. Dynamic mapping adapts to changes in source schema, while schema drift handling accommodates changes during data movement.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>15. How can we use Data Factory to deliver code to higher environments?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">We may, in principle, do this by taking the following actions:<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Establish a feature branch to hold our code base.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Once the code is verified for the Dev branch, create a pull request to integrate it.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">To create ARM templates, publish the developer&#8217;s code.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Code may be promoted to higher environments, such as staging or production, by this starting an automated <\/span><a href=\"https:\/\/www.aegissofttech.com\/insights\/devops-for-node-js-development\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span dir=\"ltr\" lang=\"EN\">CI\/CD DevOps pipeline<\/span><\/a><span dir=\"ltr\" lang=\"EN\">.<\/span><\/li>\n<\/ul>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>16. In Microsoft Azure Data Factory, which three tasks can you perform?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The three tasks that Azure Data Factory facilitates are data transformation, data transfer, and control tasks.<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Data movement activities: As the name implies, they are actions that facilitate the transfer of data between locations.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Data is copied from a source to a sink data storage, for example, using Data Factory&#8217;s Copy Activity.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Activities related to data transformation: These assist in transforming the data when it is loaded into the target or destination.<\/span><\/li>\n<\/ul>\n<p><span dir=\"ltr\" lang=\"EN\">For instance, U-SQL, Azure Functions, Stored Procedures, etc.<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">Activities for controlling flow: Activities for controlling flow aid in regulating any activity that occurs in a pipeline.<\/span><\/li>\n<\/ul>\n<p><span dir=\"ltr\" lang=\"EN\">For example, a wait action causes the pipeline to pause for a predetermined amount of time.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>17. Which two categories of computing environments does Data Factory enable for carrying out the transform tasks?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The types of computer environments that Data Factory supports for carrying out transformation operations are listed below: &#8211;<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">i) On-Demand Computing Environment: ADF offers this completely managed environment. This kind of computation forms a cluster to carry out the transformation action and, upon completion of the activity, immediately deletes it.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">ii) Bring Your Environment: If you already have the infrastructure in place for on-premises services, you may utilize ADF to manage your computing environment in this scenario.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>18. Describe the steps that make up an ETL process.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">There are four primary phases in the <\/span><a href=\"https:\/\/www.aegissofttech.com\/insights\/etl-processes-azure-data-factory\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span dir=\"ltr\" lang=\"EN\">ETL process using Azure Data Factory<\/span><\/a><span dir=\"ltr\" lang=\"EN\">:<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">i) Connect and Collect: Establish a connection with the data source or sources and transfer the data to crowdsourcing and local data storage.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">ii) Data transformation with the use of computing services like Spark, Hadoop, HDInsight, and so on.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">iii) Publish: This is the process of loading data into Azure Cosmos DB, Azure SQL databases, Azure data lakes, Azure SQL data warehouses, etc.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">iv)Monitor: PowerShell, Azure Monitor logs, API, Azure Monitor, and pipeline monitoring are all integrated into Azure Data Factory.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>19. Have you utilized Data Factory&#8217;s Execute Notebook activity? How can I give a notebook activity certain parameters?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">To transfer code to our Databricks cluster, we may run a notebook activity. The baseParameters property allows us to pass parameters to a notebook activity. The default settings from the notebook are used if the parameters are not declared or specified in the activity.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>20. What are some of the Data Factory&#8217;s helpful constructs?<\/strong><\/span><\/h3>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">parameter: Every pipeline operation can use the parameter value that is supplied to it and executed using the @parameter construct.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">coalesce: To handle null values gracefully, we may use the @coalesce construct in the expressions.<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">activity: Using the @activity construct, an activity output may be used in a later activity.<\/span><\/li>\n<\/ul>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>21. Is it possible to use ADF for code push and continuous integration and delivery, or CI\/CD?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">CI\/CD of your data pipelines utilizing GitHub and Azure DevOps is fully supported by Data Factory. This enables you to gradually create and deliver your ETL operations before releasing the final result. Once the unprocessed data has been transformed into a consumable format suitable for business use, import it into Azure Data Warehouse, Azure SQL Azure Data Lake, Azure Cosmos DB, or any other analytics engine that your company can use through its business intelligence tools.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>22. When you refer to variables in the Azure Data Factory, what do you mean?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The Azure Data Factory pipeline&#8217;s variables offer the capacity to store the values. They are available inside the pipeline and are used for the same purpose as variables in any programming language.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Two operations are performed to set or modify the values of the variables: append and set variables. A data factory has two different kinds of variables: &#8211;<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">i) System variables: These come from the Azure pipeline and are fixed variables. For instance, the name of the trigger, the pipeline id, etc. To obtain the system data needed for your use case, you must have these.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">ii) User variable: Based on your pipeline logic, a user variable is explicitly declared in your code.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>23. What do data flows for mapping mean?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">In Azure Data Factory, data transformations with a visual design are called mapping data flows. Without writing code, data engineers may create a graphical data transformation logic using data flows. Scaled-out Apache Spark clusters are used by Azure Data Factory pipelines to execute the resultant data flows as activities. The scheduling, control flow, and monitoring features of Azure Data Factory may be used to operationalize data flow processes.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">Data flow mapping offers a fully visual experience without the need for code. Scaled-out data processing is enabled by ADF-managed execution clusters running data flows. Code translation, route optimization, and data flow task execution are all handled by Azure Data Factory.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>24. In the Azure Data Factory, what is copy activity?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">One of the most often utilized and well-liked actions in the Azure data factory is copying. When moving data from one data source to another, it is utilized for ETL, or lift and shift. You can do transformations on the data as you transfer it. For instance, you receive data from a TXT\/CSV file that has 12 columns; however, you wish to preserve just seven columns when writing to your destination data source. It can be transformed such that the destination data source receives only the necessary columns.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>25.\u00a0 Could you provide further details about the Copy activity?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The following actions are carried out at a high level by the copy activity:<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">i) Examine data<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">ii) Use the data to carry out the following actions:<\/span><\/p>\n<ul>\n<li><span dir=\"ltr\" lang=\"EN\">De- and serialization of data<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Compression and decompression<\/span><\/li>\n<li><span dir=\"ltr\" lang=\"EN\">Mapping columns<\/span><\/li>\n<\/ul>\n<p><span dir=\"ltr\" lang=\"EN\">iii) Commit data to the sink or data storage at the destination. For example, Azure Data Lake<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>26. Is it possible to determine the value of a new column using the mapping of an existing column in ADF?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">With modifications to the mapping data flow, we can create a new column with the logic we want. When building a derived column, we may either update an existing one or create a new one. In the Column textbox, type the name of the column you&#8217;re creating.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">The column option allows you to override a column in your schema that already exists. To begin constructing the phrase for the derived column, click the Enter expression textbox. To construct your reasoning, you may either enter it or utilize the expression builder.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>27. In the Azure Data Factory, how is the lookup activity helpful?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The Lookup action in the ADF pipeline is frequently used to seek up configurations, and the original dataset is accessible. Additionally, it transmits the data as the activity output after retrieving it from the source dataset. Typically, the search activity&#8217;s output is utilized further in the pipeline to provide any resulting configuration or make judgments.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">In the ADF pipeline, lookup activity is essentially utilized for data fetching. Your pipeline logic would determine how you would use it in the fullest. Depending on your dataset or query, you may either obtain the entire collection of rows or only the first one.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>28. Provide more details about Azure Data Factory&#8217;s Get Metadata activity.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">Any data in the Azure Data may have its metadata retrieved using the Get Metadata activity.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">A pipeline from Synapse or a factory. The metadata obtained from the Get Metadata action can be used in later activities or validated using conditional expressions.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">It receives a dataset as input and outputs metadata data. As of right now, retrievable information for the following connections is supported. The metadata that is returned has a maximum size of 4 MB.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\">For supporting metadata that can be obtained by utilizing the Get Metadata activity, please refer to the snapshot below.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>29. How may an ADF pipeline be debugged?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">One of the most important parts of any coding activity is debugging, which is used to check the code for errors. Additionally, it offers a pipeline debugging option.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>30. What does the ADF pipeline&#8217;s breakpoint mean?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">For better understanding, let&#8217;s say you have three pipeline activities, and you wish to debug only the second activity at this point. Setting the breakpoint at the second activity will allow you to accomplish this. Click the circle that appears at the top of the activity to add a breakpoint.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>31. How does the ADF Service become used?<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The main function of ADF is to manage data copying between locally hosted relational and non-relational data sources in data centers or the cloud. Additionally, in order to meet business needs, you may utilize ADF Service to alter the ingested data. ADF Service is utilized as an ETL or <\/span><a href=\"https:\/\/www.aegissofttech.com\/insights\/choosing-right-etl-tool\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span dir=\"ltr\" lang=\"EN\">ELT tool<\/span><\/a><span dir=\"ltr\" lang=\"EN\"> for data intake in the majority of Big Data applications.<\/span><\/p>\n<h3><span dir=\"ltr\" lang=\"EN\"><strong>32. Describe the Azure data factory&#8217;s data source.<\/strong><\/span><\/h3>\n<p><span dir=\"ltr\" lang=\"EN\">The system that contains the data that is meant to be used or performed is known as the data source. Binary, text, CSV, JSON, picture files, audio, video, and proper databases are among the possible data types.<\/span><\/p>\n<p><span dir=\"ltr\" lang=\"EN\"><a title=\"SFTP enabled Azure blob storage\" href=\"https:\/\/www.aegissofttech.com\/insights\/sftp-enabled-azure-blob-storage\/\" target=\"_blank\" rel=\"noopener\">SFTP enabled Azure blob storage<\/a>, Azure data lakes, and any other database\u2014Postgres, Azure SQL, MySQL, and so on\u2014are a few examples of data sources.<\/span><\/p>\n<h2><span dir=\"ltr\" lang=\"EN\"><strong>Conclusion:<\/strong><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Hire Azure Data Factory Developers who possess a deep knowledge of these Azure Data Factory Interview Questions. They schould have a better grasp to respond to any questions with their foundations on the topic to be wiser.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" ","protected":false},"author":3,"featured_media":2966,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[22],"tags":[675,676,677,678],"class_list":["post-2963","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-azure","tag-adf-interview-questions-answer","tag-adf-questions-and-answers-for-beginners","tag-azure-data-factory-interview-for-beginners","tag-azure-data-factory-interview-questions"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/posts\/2963","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/comments?post=2963"}],"version-history":[{"count":9,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/posts\/2963\/revisions"}],"predecessor-version":[{"id":18376,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/posts\/2963\/revisions\/18376"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/media\/2966"}],"wp:attachment":[{"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/media?parent=2963"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/categories?post=2963"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aegissofttech.com\/insights\/wp-json\/wp\/v2\/tags?post=2963"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}