Preparing for an Azure Data Engineering interview? Landing a job at top tech firms like Microsoft, Walmart, EY, and Deloitte requires strong knowledge of Azure Data Factory (ADF). Below are the top 10 most commonly asked interview questions, along with concise and effective answers to help you crack your dream role as an Azure Data Engineer.
Q1. How would you ingest large datasets from on-prem SQL Server to Azure Data Lake using ADF?
Answer:
Use the Copy Activity in ADF with a Self-hosted Integration Runtime to securely connect to the on-prem SQL Server. Set up source and sink datasets accordingly. To manage large datasets, enable parallel copy, staging, and batching options. You can also use partitioning to break data into manageable chunks for performance and scalability.
Q2. How would you merge data from multiple sources (SQL Server, Blob Storage, API) into Azure SQL Data Warehouse?
Answer:
Create individual Copy Activities or Data Flows for each source. Use Mapping Data Flows to perform joins, union, and transformations. Ensure schema alignment and use staging layers like Azure Data Lake if necessary. Finally, load the transformed data into Azure Synapse Analytics (formerly SQL DW).
Q3. How would you design a pipeline to move data from Azure Blob Storage to Azure SQL Database with incremental loads?
Answer:
Use Watermarking or Change Tracking strategies. Maintain a column (e.g., LastModifiedDate) and store the latest watermark in ADF variables or Azure SQL table. Filter source data using this value. Use Lookup + Copy Activity to retrieve and move only new/changed records.
Q4. How would you implement logging and monitoring in ADF pipelines?
Answer:
ADF integrates with Azure Monitor, Log Analytics, and Activity Runs. You can enable diagnostic settings to capture logs. Additionally, use custom logging by adding activities that log status to SQL or Blob. You can also trigger alerts based on pipeline failures or thresholds.
Q5. How would you integrate a third-party REST API in ADF and handle rate limiting?
Answer:
Use the Web activity or Copy Activity (with REST linked service) to call APIs. For rate limiting, configure retry policies, timeouts, and wait/pause using Until or Wait activities. You can also handle pagination with dynamic parameters and use ForEach loops for batch API calls.
Q6. How would you handle schema drift in ADF data transfers?
Answer:
ADF supports schema drift in Mapping Data Flows by using wildcard column mappings. Use “Auto Mapping” and “Allow schema drift” options to make pipelines adaptable to changes in schema. Consider storing schema metadata externally to drive dynamic mapping.
Q7. How would you perform complex transformations (joins, aggregations) using ADF Data Flows?
Answer:
Use Mapping Data Flows, which provide a UI for building transformations. You can perform joins, aggregates, window functions, pivot/unpivot, etc. Use source transformations for input, and apply the required transformation activities, followed by writing the results to the sink.
Q8. How would you design a parallel data processing pipeline in ADF?
Answer:
Use ForEach Activity with the “Batch Count” setting to execute activities in parallel. You can also use Data Flow partitioning, and leverage ADF’s parallel copy capabilities. Break down data into chunks or files and process them concurrently for faster throughput.
Q9. How would you optimize the performance of a pipeline moving data between Blob Storage and Azure SQL Database?
Answer:
Enable staged copy using Azure Blob as intermediate storage.
Use polybase or bulk insert when targeting Synapse.
Tune batch size, parallelism, and data partitioning.
Avoid unnecessary transformations in Copy Activity.
Monitor with Integration Runtime metrics and activity run time.
Q10. How would you ensure data security and compliance when transferring sensitive data using ADF?
Answer:
Use Managed Identity or Azure Key Vault for secure credential management.
Enable TLS encryption in transit and Azure Storage encryption at rest.
Implement network security rules, such as private endpoints or VNet Integration.
Mask or encrypt sensitive fields before transfer.
Use audit logging and RBAC to track access and enforce security.
Conclusion
Mastering Azure Data Factory is essential to succeed in an Azure Data Engineer interview. These questions not only test your ADF knowledge but also your practical problem-solving ability. Make sure to practice hands-on, review pipeline performance, and understand integration scenarios.
Good luck with your interview prep!
Join Our Telegram Group (1.9 Lakhs + members):- Click Here To Join
For Experience Job Updates Follow – FLM Pro Network – Instagram Page
For All types of Job Updates (B.Tech, Degree, Walk in, Internships, Govt Jobs & Core Jobs) Follow – Frontlinesmedia JobUpdates – Instagram Page
For Healthcare Domain Related Jobs Follow – Frontlines Healthcare – Instagram Page
For Major Job Updates & Other Info Follow – Frontlinesmedia – Instagram Page
