AZURE data engineer interview questions

Category : Interview Questions | Sub Category : Azure Data Engineer Interview questions | By Prasad Bonam Last updated: 2023-09-03 09:29:20 Viewed : 288

Preparing for an Azure Data Engineer interview requires a strong understanding of Azure services and data engineering concepts. Here are some interview questions you might encounter:

Azure Data Services:

1. What are the key Azure data services, and how do they fit into the data engineering landscape?

2. Explain the differences between Azure Data Factory, Azure Data bricks, and Azure Synapse Analytics.

3. What is Azure Data Lake Storage, and how does it differ from Azure Blob Storage?

4. How can you move data from on-premises databases to Azure using Azure Data Factory?

Data Ingestion:

5. Describe the various methods for ingesting data into Azure, including batch and real-time.

6. What is Azure Event Hubs, and how can it be used for real-time data ingestion?

Data Transformation and Processing:

7. Explain the purpose of Azure Data Factory Data Flows and Data bricks notebooks in data transformation.

8. How can you process and transform data using Azure Functions in a data engineering pipeline?

Data Warehousing:

9. What is Azure Synapse Analytics (formerly SQL Data Warehouse), and how does it enable data warehousing?

10. Describe the differences between Azure Synapse Analytics on-demand and provisioned SQL pools.

Data Orchestration and ETL:

11. How do you orchestrate and schedule data pipelines in Azure Data Factory?

12. What are the best practices for designing and implementing ETL processes in Azure?

Monitoring and Optimization:

13. How do you monitor the performance and cost of Azure data pipelines and workloads?

14. What are some optimization techniques for improving the performance of data processing in Azure?

Data Security and Compliance:

15. Explain how Azure services like Azure Key Vault and Azure Managed Identity enhance data security.

16. What is Azure Purview, and how does it assist with data governance and compliance?

Data Integration and Hybrid Solutions:

17. How can you create hybrid data solutions that connect on-premises data sources to Azure services?

Data Lake Architecture:

18. Describe the architecture of a modern data lake on Azure, including key components and best practices.

Data Migration:

19. What are the common challenges and strategies for migrating large datasets to Azure?

Data Engineering Tools:

20. Have you worked with popular data engineering tools like Apache Spark, Apache Kafka, or Apache Hadoop? Describe your experience and use cases.

Scenario-based Questions:

21. Provide an example of a complex data engineering problem you have solved using Azure services. How did you approach it?

22. How would you design a data engineering solution for processing and analysing large IoT data streams in real-time using Azure?

Remember to prepare not only by answering these questions but also by demonstrating your problem-solving skills, ability to design data pipelines, and your knowledge of best practices in data engineering. Tailor your responses to your specific experiences and projects to showcase your expertise effectively.