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Azure Data Engineering Interview Questions

✅ What Is Azure Data Engineering, and Why Is It Important?
✅ What are the core Azure data services, and how do they differ in functionality?
✅ How can you create a simple data pipeline using Azure Data Factory?
✅ What is ETL, and how does it work?
✅ What is the difference between ETL and ELT?
✅ What are the key differences between Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database?
✅ When should you use Azure Data Lake Storage instead of Azure Blob Storage?
✅ What are the fundamental SQL commands for data extraction in Azure, and how do they work?
✅ How does Azure Data Factory handle batch ingestion?
✅ What is the difference between batch data ingestion and real-time data ingestion in Azure?
✅ How does Azure Databricks handle large-scale data transformation using Apache Spark?
✅ What are the key differences between transformations and actions in Apache Spark within Azure Databricks?
✅ How does Azure Data Factory orchestrate data workflows, and what are its key components?
✅ What are the different triggers in Azure Data Factory, and how do they work?
✅ How do Azure Synapse Analytics and Azure Databricks differ in architecture and primary use cases?
✅ When should you use Azure Synapse versus. Databricks for real-time data processing?
✅ What is Role-Based Access Control (RBAC) in Azure, and how does it help secure data?
✅ How does Azure ensure data security using encryption, and what is the role of Azure Purview in governance?
✅ What is data partitioning, and how does it improve performance in Azure data processing?
✅ How can indexing and caching optimize query performance in Azure Synapse Analytics?
✅ How does Azure Stream Analytics process real-time data from Event Hubs, and what performance optimizations are possible?
✅ How can you implement an event-driven architecture using Azure Event Hubs and Azure Functions for real-time data processing?
✅ How can you integrate Azure Synapse Analytics with Apache Spark for advanced big data analytics, and what are the key benefits?
✅ How can you optimize the cost of an Azure data engineering solution while maintaining performance and scalability?
✅ What are the best practices for performance tuning in Azure data engineering workflows?

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