Introduction
Azure Data Factory (ADF) is a cloud-based data integration service that allows users to create, schedule, and manage data workflows. As organizations increasingly adopt cloud-based data solutions, ADF has become a critical skill for data engineers, ETL developers, and cloud architects.
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Section 1: Azure Data Factory Fundamentals
1. What is Azure Data Factory (ADF)?
Azure Data Factory is a fully managed, serverless data integration service that enables users to create, schedule, and orchestrate data pipelines. It supports hybrid data integration, allowing data movement between on-premises and cloud sources.
2. What are the key components of ADF?
- Pipelines – Logical grouping of activities.
- Activities – Tasks performed within a pipeline (e.g., copy, transformation).
- Datasets – Named views of data structures.
- Linked Services – Connection strings to external data sources.
- Integration Runtimes – Compute infrastructure for data movement.
- Triggers – Schedule or event-based pipeline execution.
3. What is the difference between Azure Data Factory and SSIS?
| Feature | Azure Data Factory | SQL Server Integration Services (SSIS) |
| Deployment | Cloud-based | On-premises/Cloud via Azure-SSIS IR |
| Scalability | Serverless, auto-scaling | Manual scaling required |
| Pricing | Pay-as-you-go | License-based |
| Data Sources | Hybrid (cloud + on-prem) | Primarily on-premises |
4. What types of transformations can be performed in ADF?
- Mapping Data Flows – Code-free transformations.
- Stored Procedures – SQL-based transformations.
- HDInsight/Hive/Pig – Big data processing.
- Databricks Notebooks – Advanced analytics.
5. How does ADF support hybrid data integration?
ADF uses Self-Hosted Integration Runtime (SHIR) to connect to on-premises data sources like SQL Server, Oracle, and file systems.
Section 2: ADF Pipeline & Activities
6. What is a pipeline in ADF?
A pipeline is a logical grouping of activities that perform a data workflow (e.g., ETL process).
7. What are the different types of activities in ADF?
- Data Movement Activities – Copy data between sources.
- Data Transformation Activities – Data Flow, Stored Procedure, Databricks.
- Control Activities – Execute Pipeline, ForEach, If Condition.
8. How do you schedule a pipeline in ADF?
Using Triggers:
- Schedule Trigger – Runs at fixed intervals.
- Event-Based Trigger – Runs on blob creation/deletion.
- Tumbling Window Trigger – For time-series data processing.
9. What is a Lookup Activity in ADF?
A Lookup activity retrieves a dataset from a source (e.g., SQL table) and passes it to subsequent activities.
10. How does the ForEach activity work?
It iterates over a collection (e.g., list of files) and executes activities in a loop.
Section 3: Data Flows & Transformations
11. What are Mapping Data Flows in ADF?
Mapping Data Flows provide a visual, code-free way to transform data using Spark clusters.
12. What are the different transformations in Mapping Data Flows?
- Source/Sink – Data input/output.
- Filter – Row-level filtering.
- Derived Column – Create new columns.
- Aggregate – Group by operations.
- Join – Merge datasets.
13. How do you debug a Data Flow?
Enable Data Flow Debug Mode to test transformations before publishing.
Section 4: Monitoring & Error Handling
14. How do you monitor ADF pipelines?
Using Azure Monitor, ADF Monitoring Hub, and Log Analytics.
15. What are the common ADF pipeline failure reasons?
- Incorrect linked service credentials.
- Network connectivity issues.
- Data source unavailability.
- Activity timeout errors.
16. How do you handle errors in ADF?
- Retry Policy – Set retry attempts.
- Fail Activity on Error – Stop pipeline on failure.
- Error Logging – Log errors to a database.
Section 5: Advanced ADF Concepts
17. What is Delta Lake integration in ADF?
ADF supports Delta Lake for ACID transactions and schema enforcement in data lakes.
18. How does ADF integrate with Azure Synapse Analytics?
ADF pipelines can load data into Synapse using PolyBase or COPY commands.
19. What is Parameterization in ADF?
Dynamic values can be passed to pipelines using parameters (e.g., file paths, table names).
Conclusion
Azure Data Factory is a powerful tool for cloud-based ETL and data integration. Mastering these ADF interview questions will help you excel in technical discussions and certification exams.
For additional preparation, Dumpsarena provides high-quality ADF exam dumps and study materials to help you succeed.
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