Medium-level cloud & tools questions from real data engineering interviews.
These medium cloud & tools questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview. This set leans toward the medium-difficulty band most real interviews actually live in (27 of 27). Recurring themes are partition, join, and spark — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 18 companies including Capco and Virtusa. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 27 curated questions: 0 easy, 27 medium. The balanced mix of difficulties makes this set suitable for engineers at any career stage.
The most frequently tested areas in this set are partition (19), join (5), spark (4), window (4), etl (3), and bigquery (3). Focusing on these topics will give you the highest return on your preparation time.
Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
What is the role of AWS Lambda in a data engineering pipeline?
Copy Large Files from On-Premises to Azure in ADF
Data Load in Synapse Table?
Describe Amazon Athena and how it interacts with S3.
Describe the use of side inputs in Dataflow.
Describe your experience with cloud platforms like AWS, Azure, or GCP
Difference between pipelines and data flows in ADF
Discuss S3's advantages, including scalability and durability.
Explain how AWS Glue interacts with on-premises SQL databases to extract data efficiently.
Explain how using a staging area in S3 can help.
Explain how you debug failed pipelines in ADF.
Explain job bookmarking in AWS Glue. How does it help in incremental data processing?
Explain the key components of Apache Beam in the context of Google Dataflow.
Explain the role of Glue Catalog in Athena.
Explain using AWS Glue for ETL. What challenges might you face with large datasets?
How can you increase parallelism in ADF pipelines?
How do you ensure message ordering in Kinesis Streams?
How do you handle data cleanup and lifecycle management in S3?
How do you handle data using AWS S3?
How do you manage data storage in AWS?
How do you merge data from different sources in ADF while maintaining data quality?
How would you optimize an ADF pipeline for high performance?
How would you migrate 1TB of data using ADF?
How would you optimize cost when using AWS for large-scale data processing?
Lambda vs. Glue: Discuss use cases for both services.
What alternatives to Kinesis would you consider for real-time data ingestion?
What integration challenges might you face with Glue Catalog in non-AWS environments?
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