Table of Contents
Basic Level Questions (15 Questions)
What is Azure Data Factory (ADF)?
Answer: Azure Data Factory is a cloud-based ETL (Extract, Transform, Load) service provided by Microsoft that allows users to create data-driven workflows for orchestrating data movement and transforming data at scale.
What are the key components of Azure Data Factory?
Answer: Key components include Pipelines, Datasets, Linked Services, Activities, Triggers, and Integration Runtime.
What is a pipeline in Azure Data Factory?
Answer: A pipeline is a logical grouping of activities that perform a unit of work. Together, the activities in a pipeline perform a task, such as moving data from one place to another or transforming data.
What is a dataset in Azure Data Factory?
Answer: A dataset in ADF represents a data structure within the data store that points to the data you want to use in your activities, such as a table in a SQL database or a file in Azure Blob Storage.
Explain the concept of Linked Services in ADF.
Answer: Linked Services are connections to data sources, such as Azure Blob Storage or Azure SQL Database, which define the connection details that the Data Factory service uses to connect to external data sources.
What is an Integration Runtime in Azure Data Factory?
Answer: Integration Runtime (IR) is the compute infrastructure used by ADF to provide data movement, activity dispatch, and SSIS package execution. There are three types: Azure, Self-hosted, and SSIS Integration Runtime.
What types of triggers are available in Azure Data Factory?
Answer: Azure Data Factory supports different types of triggers such as Schedule Trigger, Tumbling Window Trigger, Event-based Trigger, and Manual Trigger.
How does Azure Data Factory handle data transformation?
Answer: Data transformation in ADF can be handled using Data Flows, which provide a way to visually design data transformation logic, or by integrating with Azure Databricks, HDInsight, or using custom scripts.
What is the difference between Copy Activity and Data Flow in ADF?
Answer: Copy Activity is used to copy data from a source to a destination, while Data Flow is used for more complex data transformation tasks, allowing for data manipulation and enrichment.
What is a Control Flow in Azure Data Factory?
Answer: Control Flow orchestrates the execution flow of a pipeline by defining dependencies and determining the order of execution of activities within a pipeline.
What is a self-hosted Integration Runtime in ADF?
Answer: A self-hosted Integration Runtime is a data gateway that allows you to integrate with on-premises data sources, enabling data movement between on-premises and cloud-based services.
Explain the concept of Tumbling Window Trigger in ADF.
Answer: Tumbling Window Trigger is a trigger type that fires at regular intervals based on a specified time window, ensuring each time window processes data independently without overlap.
What is the purpose of the Azure Data Factory Monitor?
Answer: The ADF Monitor allows you to track the progress and status of pipelines, activities, and triggers, providing insights into execution details, successes, and failures.
How does Azure Data Factory support data integration across various services?
Answer: ADF supports data integration by connecting to over 90+ built-in connectors for cloud and on-premises data sources, enabling seamless data movement and transformation.
Can you explain the use of parameters in Azure Data Factory?
Answer: Parameters in ADF allow for dynamic and flexible pipelines by enabling users to pass values into pipelines, datasets, and linked services at runtime.
Intermediate Level Questions (15 Questions)
What is the role of a Lookup Activity in Azure Data Factory?
Answer: Lookup Activity retrieves data from a data source and stores it for use later in the pipeline, often used to retrieve configuration settings or to look up values needed for the pipeline’s execution.
How do you handle error handling in Azure Data Factory?
Answer: Error handling in ADF can be managed using activities like If Condition, Switch, Until, Retry, and by setting up alerts and logging mechanisms in the pipeline.
Explain the use of the ForEach activity in ADF.
Answer: The ForEach activity allows you to iterate over a collection of items and execute activities for each item in the collection, enabling looping logic within a pipeline.
How do you implement incremental data load in Azure Data Factory?
Answer: Incremental data loads can be implemented by capturing the changes in the source data using watermark columns (like timestamps) or change data capture mechanisms, then processing only the changed or new data.
What is a Data Flow Debug feature in Azure Data Factory?
Answer: Data Flow Debug allows you to interactively test, troubleshoot, and debug data flows in real-time within the development environment before deploying them to production.
How does Azure Data Factory integrate with Azure Key Vault?
Answer: Azure Data Factory integrates with Azure Key Vault to securely store and manage secrets, keys, and certificates, which can be used to securely access data sources within ADF.
What is a Web Activity in Azure Data Factory?
Answer: A Web Activity in ADF allows you to make HTTP requests to REST APIs and services, enabling the integration of external services into your data pipeline.
Explain the concept of Data Flow Activity in ADF.
Answer: Data Flow Activity in ADF allows for the creation of complex data transformations visually using a mapping data flow, enabling users to perform operations such as joins, aggregations, and data cleansing.
What is the use of the Wait Activity in Azure Data Factory?
Answer: The Wait Activity pauses the execution of a pipeline for a specified period, which can be useful for scenarios where you need to wait for a condition to be met before proceeding with the next activity.
How do you schedule a pipeline in Azure Data Factory?
Answer: Pipelines in ADF can be scheduled using triggers, such as Schedule Triggers for time-based schedules, Tumbling Window Triggers for fixed intervals, and Event Triggers for event-driven executions.
What is PolyBase in Azure Data Factory, and how is it used?
Answer: PolyBase is a technology that allows for the querying and import of data from external sources directly into Azure SQL Data Warehouse using T-SQL. In ADF, PolyBase is used to efficiently load large amounts of data into Azure Synapse Analytics.
How does ADF support version control and CI/CD?
Answer: ADF supports version control by integrating with Git repositories, allowing for collaboration, branching, and pull requests. CI/CD pipelines can be set up using Azure DevOps to automate the deployment of ADF pipelines.
What are the different types of Integration Runtimes in ADF, and when should you use each?
Answer: The three types are Azure Integration Runtime for cloud data movements, Self-hosted Integration Runtime for on-premises data sources, and SSIS Integration Runtime for executing SSIS packages in the cloud.
Explain how ADF handles schema drift.
Answer: ADF handles schema drift by dynamically detecting changes in the schema and adapting data flows to accommodate new or removed columns without manually updating the pipeline.
How do you implement data partitioning in Azure Data Factory?
Answer: Data partitioning in ADF can be implemented using partitioning strategies in Data Flows, such as hash, round-robin, or custom expressions, to distribute data across partitions for parallel processing.
Advanced Level Questions (10 Questions)
What is the use of Stored Procedure Activity in ADF?
Answer: Stored Procedure Activity allows you to execute stored procedures in a database, enabling complex data transformations and business logic to be applied as part of the pipeline.
How do you optimize performance in Azure Data Factory?
Answer: Performance optimization can be achieved by using parallelism, partitioning data, optimizing data movement by using PolyBase, reducing data transformation complexity, and using the right Integration Runtime for the workload.
Explain the concept of a self-hosted Integration Runtime in a hybrid data integration scenario.
Answer: In a hybrid scenario, a self-hosted Integration Runtime is deployed within an on-premises network to enable secure data movement and transformation between on-premises and cloud environments, bridging the gap between on-premises data and Azure services.
What is the difference between Mapping Data Flows and Wrangling Data Flows in ADF?
Answer: Mapping Data Flows provide a scalable, graphical data transformation capability within ADF, while Wrangling Data Flows are more interactive, allowing for data exploration and preparation using Power Query Online.
How do you implement dynamic pipelines in ADF?
Answer: Dynamic pipelines in ADF are implemented using parameters, variables, and expressions to create reusable components that can adapt to different data sources, transformations, and outputs based on runtime inputs.
How can you secure data in transit and at rest in Azure Data Factory?
Answer: Data in transit is secured using SSL/TLS encryption, while data at rest can be secured by leveraging encryption provided by Azure services, such as Azure Storage encryption, SQL Database TDE, and integrating with Azure Key Vault for key management.
What are the best practices for monitoring and troubleshooting ADF pipelines?
Answer: Best practices include using the ADF Monitor for real-time monitoring, setting up alerts and logging, reviewing activity and pipeline run history, using the Performance Analyzer for data flows, and implementing retries and error handling strategies.
How do you manage pipeline dependencies in Azure Data Factory?
Answer: Pipeline dependencies can be managed using activities like Wait, Until, If Condition, and Switch, as well as by chaining pipelines together using Execute Pipeline Activity.
Explain the concept of PolyBase ELT in the context of ADF and Synapse Analytics.
Answer: PolyBase ELT refers to the process of extracting and loading data into Azure Synapse Analytics using PolyBase, and then transforming the data within Synapse using T-SQL for large-scale data processing.
How would you implement a CI/CD pipeline for Azure Data Factory using Azure DevOps?
Answer: A CI/CD pipeline can be implemented by connecting ADF to a Git repository for version control, creating build and release pipelines in Azure DevOps to automate the deployment of ADF artifacts, and using ARM templates or the ADF API for deployment.
Scenario-Based and Practical Questions (10 Questions)
Describe a scenario where you had to integrate Azure Data Factory with Azure Databricks.
Answer: (Answer will vary based on experience; candidates can describe how they used ADF to orchestrate a data pipeline that ingested data into Azure Data Lake, used Databricks for processing and transformation, and then loaded the data into a data warehouse.)
How would you design a pipeline to handle both batch and streaming data in ADF?
Answer: The pipeline would use a combination of batch processing with scheduled triggers for historical data and Event-based triggers or Azure Stream Analytics for real-time data, ensuring data is ingested, processed, and stored correctly.
What approach would you take to optimize a slow-running ADF pipeline?
Answer: Steps include identifying bottlenecks using monitoring tools, optimizing data flows, increasing parallelism, tuning Integration Runtime settings, reducing data movement, and possibly refactoring complex transformations.
How would you implement data lineage in Azure Data Factory?
Answer: Data lineage can be implemented by tracking data movement and transformations using metadata tables, logging activities, leveraging ADF’s built-in monitoring features, and integrating with tools like Azure Purview for end-to-end lineage tracking.
Explain how you would handle schema changes in a source system within an existing ADF pipeline.
Answer: Handling schema changes can involve setting up schema drift detection, using dynamic mapping in data flows, leveraging flexible schemas in datasets, and implementing error handling to manage unexpected changes.
What strategies would you use to implement data masking or data obfuscation in Azure Data Factory?
Answer: Data masking can be implemented by using custom transformations in Data Flows, applying encryption or hashing techniques, and leveraging ADF’s integration with Azure Key Vault to handle sensitive information securely.
How would you design an ADF pipeline to process and load data from multiple sources into a data warehouse?
Answer: The pipeline would include multiple source-linked services and datasets, use Copy Activity to ingest data, Data Flows for transformation, and utilize Azure Synapse Analytics or SQL Data Warehouse as the destination.
What is your approach to debugging a failed pipeline run in Azure Data Factory?
Answer: Debugging involves checking the pipeline run history, analyzing activity logs, reviewing error messages, using Data Flow Debug mode if applicable, and correcting issues in configurations, connections, or transformations.
How would you implement a scenario where data needs to be archived after processing in Azure Data Factory?
Answer: After processing, data can be archived by using a Copy Activity to move the data to an Azure Blob Storage archive tier or by using Data Flow to transfer and optionally compress the data before archiving.
Can you describe a complex data transformation scenario you implemented using Azure Data Factory?
Answer: (Answer will vary based on experience; candidates should discuss a specific transformation challenge, such as normalizing unstructured data, handling multi-step transformations, or integrating with machine learning models.)
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