Spark & Big Data questions from Nihilent data engineering interviews.
These spark & big data questions are sourced from Nihilent data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (2 of 6 are tagged hard). Recurring themes are spark, partition, and join — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Coforge and Freecharge, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 6 curated questions: 2 easy, 2 medium, and 2 hard. The balanced mix of difficulties makes this set suitable for engineers at any career stage.
The most frequently tested areas in this set are spark (5), partition (4), join (2), optimization (2), sql (1), and lakehouse (1). 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. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. 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.
Can you explain the architecture of Apache Spark and its components?
Accumulators - use as shared variable for write-only operations
Broadcast join - how it optimizes joins
Databricks notebooks vs. Fabric notebooks - differences
Schema evolution - techniques for handling schema changes in PySpark
Writing Excel sheets to Delta tables in Databricks
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