Spark & Big Data questions from EPAM data engineering interviews.
These spark & big data questions are sourced from EPAM data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (2 of 3 are tagged hard). Recurring themes are spark, optimization, and partition — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 3 curated questions: 1 easy, and 2 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (3), optimization (2), and partition (2). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. 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.
Describe how you would optimize slow-running Spark jobs in a distributed environment.
Explain your approach to monitoring and logging Spark jobs in AWS. What tools would you use to identify performance bottlenecks?
How do you implement incremental updates in a data lake using AWS services and Spark?
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