AWS DE Questions

This post details AWS data engineering interview and highlights the most common concepts you can expect to be asked in interview processes.

1. Start by providing a concise introduction to your professional projects, emphasizing your role as a data engineer.
2. Share your knowledge of cloud platforms (AWS, GCP, Azure) as it pertains to data engineering.
3. Discuss the ETL (Extract, Transform, Load) tools you have experience with.
4. Present an end-to-end live data engineering project pipeline to showcase your knowledge and the approaches you’ve taken.
5. Prepare for data structure and algorithms (DSA) questions to evaluate your language coding skills and logical reasoning.
6. Describe data partitioning in Spark and how it is implemented.
7. Explain how the concepts of repartition and coalesce function in Spark.
8. Provide an overview of Spark’s architecture, highlighting its key components.
9. Explain groupBy and reduceBy operations in Spark.
10. Differentiate between Dynamic-Frames and Data-Frames in data engineering tasks.
11. Discuss fundamental data warehousing concepts relevant to your role.
12. Expect various aggregation operations questions that can be performed on Data-Frames.
13. Data modeling understanding.

If you specialize in AWS data engineering services, be prepared for additional questions:

1. Elaborate on the key components of AWS Glue and their roles in the ETL (Extract, Transform, Load) process.
2. Detail how AWS Glue manages data extraction, transformation, and loading, providing a comprehensive understanding of the ETL process.
3. Explain how AWS Glue handles schema evolution in data sources and its impact on ETL processes.
4. Describe AWS Glue’s approach to error handling and maintaining data quality throughout the ETL processes.
5. Why and how to use bookmarks in AWS Glue.
6. Various approaches to pass parameters to a AWS Glue Job.
7. Data distribution in AWS Redshift.
8. Back up options for AWS Redshift.
9. What is Redshift Spectrum and how & when to use them.
10. Compare AWS EC2 vs AWS Lambda. Discuss different use cases.
11. AWS Lambda limitation (such as Memory Allocation, Execution Time Limits, Cold Start & Resource Limits).
12. Explain the concept of AWS Data pipeline and how does they differ from AWS Glue.

Please ensure that when responding to questions about project architecture, data modeling, or an ETL pipeline, you request the opportunity to share your screen and provide a live presentation. This will allow the interviewer to gain a more comprehensive understanding of your work and how meticulously you’ve executed it.

These are few of the key questions that the interviewer will use to assess your skills. When discussing domain knowledge, strive to be both concise and comprehensive.